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30 Agents Every AI Engineer Must Build
30 Agents Every AI Engineer Must Build
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Full star icon Full star icon Full star icon Full star icon Half star icon 4.5
By Imran Ahmad
March 2026 | 542 pages
Icon Design and implement 30 proven agent architectures used in real-world production environments
Icon Build scalable, secure, and resilient agent workflows that move beyond simple chat interfaces
Icon Master core agentic principles—perception, memory, reasoning, and planning—to create truly autonomous systems
Chapter 1: Foundations of Agent Engineering Chevron down icon Chevron up icon
Chapter 1: Foundations of Agent Engineering Introducing agents Architecture of agents Interoperability protocols The Agent Development Lifecycle The evolution of agent interaction paradigms The Agentic AI Progression Framework Real-world business impact Summary Get This Book's PDF Version and Exclusive Extras
Chapter 2: The Agent Engineer's Toolkit Chevron down icon Chevron up icon
Chapter 2: The Agent Engineer's Toolkit Agent development frameworks: The architect's blueprint LLMs: The cognitive core Supporting infrastructure: The agent ecosystem Cloud-native agent development platforms Summary
Chapter 3: The Art of Agent Prompting Chevron down icon Chevron up icon
Chapter 3: The Art of Agent Prompting From instructions to constitutions: The new role of prompts The two-layer prompt architecture: System and user prompts The PTCF blueprint: A framework for principled prompt design Designing thinking agents Teaching by example: Few-shot learning as a catalyst for agent adaptability Making reasoning visible: Chain-of-thought and tree-of-thought Architecting collaboration: Prompt-driven communication protocols Iterating and evaluating prompts Summary Get This Book's PDF Version and Exclusive Extras
Chapter 4: Agent Deployment and Responsible Development Chevron down icon Chevron up icon
Chapter 4: Agent Deployment and Responsible Development Scaling agent systems Security and privacy considerations Ethical agent development: building responsible AI systems Balancing ethics and performance Toolchain quick reference Summary
Chapter 5: Foundational Cognitive Architectures Chevron down icon Chevron up icon
Chapter 5: Foundational Cognitive Architectures Technical requirements The Autonomous Decision-Making agent The Planning agent: Orchestrating dynamic workflows with adaptive intelligence The Memory-Augmented agent Comparative analysis of architectures Engineering best practices Summary Get This Book's PDF Version and Exclusive Extras
Chapter 6: Information Retrieval and Knowledge Agents Chevron down icon Chevron up icon
Chapter 6: Information Retrieval and Knowledge Agents Knowledge Retrieval agents Document Intelligence agents Scientific Research agents The knowledge agent spectrum Summary
Chapter 7: Tool Manipulation and Orchestration Agents Chevron down icon Chevron up icon
Chapter 7: Tool Manipulation and Orchestration Agents Agents in action: Tool invocation patterns Tool discovery and selection algorithms Error handling in tool integration The chain-of-agents orchestrator Memory-augmented multi-agent systems Conflict resolution mechanisms The agentic workflow system Summary Get This Book's PDF Version and Exclusive Extras
Chapter 8: Data Analysis and Reasoning Agents Chevron down icon Chevron up icon
Chapter 8: Data Analysis and Reasoning Agents The Data Analysis agent The Verification and Validation agent The General Problem Solver Case study: News fact-checking assistant - An agent for journalistic integrity Case study: Cross-disciplinary hypothesis generation — Applying ecological resilience to power grid stability Summary
Chapter 9: Software Development Agents Chevron down icon Chevron up icon
Chapter 9: Software Development Agents Market context and emerging trends Code-Generation agents Compliance-Driven agents The Self-Improving agent Summary Get This Book's PDF Version and Exclusive Extras
Chapter 10: Conversational and Content Creation Agents Chevron down icon Chevron up icon
Chapter 10: Conversational and Content Creation Agents Technical requirements The Conversational agent The Content Creation agent Summary
Chapter 11: Multi-Modal Perception Agents Chevron down icon Chevron up icon
Chapter 11: Multi-Modal Perception Agents Technical requirements Vision-Language agents Audio Processing agents Physical World Sensing agents Summary Get This Book's PDF Version and Exclusive Extras
Chapter 12: Ethical and Explainable Agents Chevron down icon Chevron up icon
Chapter 12: Ethical and Explainable Agents Technical requirements The Ethical Reasoning agent The Explainable agent Summary
Chapter 13: Healthcare and Scientific Agents Chevron down icon Chevron up icon
Chapter 13: Healthcare and Scientific Agents Technical requirements The Healthcare Intelligence agent The Scientific Discovery agent Summary Get This Book's PDF Version and Exclusive Extras
Chapter 14: Financial and Legal Domain Agents Chevron down icon Chevron up icon
Chapter 14: Financial and Legal Domain Agents Technical requirements The Financial Advisory agent The Legal Intelligence agent Summary
Chapter 15: Education and Knowledge Agents Chevron down icon Chevron up icon
Chapter 15: Education and Knowledge Agents Technical requirements The Education Intelligence agent The Collective Intelligence agent Summary Get This Book's PDF Version and Exclusive Extras
Chapter 16: Embodied and Physical World Agents Chevron down icon Chevron up icon
Chapter 16: Embodied and Physical World Agents Technical requirements Architectural foundations: The depth–breadth divide The Embodied Intelligence agent The Domain-Transforming Integration agent Case study: Autonomous drone mission planning in Ottawa's winter conditions Summary
Chapter 17: Epilogue: The Future of Intelligent Agents Chevron down icon Chevron up icon
Chapter 17: Epilogue: The Future of Intelligent Agents Emerging paradigms Strategic implementation Summary Get This Book's PDF Version and Exclusive Extras
Index Chevron down icon Chevron up icon
Index
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Python Illustrated
Python Illustrated
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Full star icon Full star icon Full star icon Full star icon Half star icon 4.5
By Maaike van Putten
February 2026 | 432 pages
Icon Follow an adorable, illustrated teaching cat with sharp coding instincts as your guide
Icon Build confidence and coding skills with step-by-step explanations that gradually increase your understanding
Icon Reinforce your learning through mini-projects and exercises at the end of every chapter
Icon Purchase of the print or Kindle book includes a free PDF eBook
Introduction Chevron down icon Chevron up icon
Introduction
Get Your Computer Ready to Code Python Chevron down icon Chevron up icon
Get Your Computer Ready to Code Python Exploring Python features Technical requirements The terminal Opening a terminal Speaking the terminal’s language Is Python already purring on your system? Windows users: a tiny hiccup Installing Python Writing our first program Running your first Python program Working with an IDE Introducing Visual Studio Code (commonly called VS Code) Creating and running a program with an IDE Alternatives for when you can’t use a computer Quiz Exercises Summary
Understanding Variables and Data Types Chevron down icon Chevron up icon
Understanding Variables and Data Types What are variables? Naming variables Basic data types Boolean values Comments in code Quiz Exercises Summary Get this book’s PDF version and more
Working with Conditional Statements Chevron down icon Chevron up icon
Working with Conditional Statements The if statement The match statement The ternary operator Common mistakes and how to avoid them Quiz Exercises Summary
Using Lists, Tuples, and Dictionaries Chevron down icon Chevron up icon
Using Lists, Tuples, and Dictionaries Lists Tuples Dictionaries: key-value pairs Choosing between lists, dictionaries, and tuples Combining different data types Common mistakes and how to avoid them Quiz Exercises Summary Get this book’s PDF version and more
Iterating with Loops Chevron down icon Chevron up icon
Iterating with Loops The problem with repetitive code Introducing loops Looping over collections Nested loops Loop control statements Common mistakes and how to avoid them Quiz Exercises Summary
Writing Functions and Using Built-In Functions Chevron down icon Chevron up icon
Writing Functions and Using Built-In Functions Understanding functions Writing functions Function parameters in detail Scope of variables Built-in functions overview Documentation and docstrings Quiz Exercises Summary Get this book’s PDF version and more
Handling Files and Exceptions Chevron down icon Chevron up icon
Handling Files and Exceptions Different types of files to handle Opening and closing files Reading files Writing to files Working with different file formats Handling errors and exceptions Quiz Exercises Summary
Creating and Using Classes Chevron down icon Chevron up icon
Creating and Using Classes Introduction to Object-Oriented Programming (OOP) Coding classes and objects Encapsulation in Python Dunder (magic) methods Final notes on working with classes Quiz Exercises Summary Get this book’s PDF version and more
Understanding Inheritance Chevron down icon Chevron up icon
Understanding Inheritance Why inheritance? Basic syntax of inheritance Types of inheritance Inheritance best practices Quiz Exercises Summary
Debugging Our Code Chevron down icon Chevron up icon
Debugging Our Code Why debugging matters Common types of bugs Understanding error messages Tools and techniques for debugging Debugging workflow Quiz Exercises Summary
Next Steps Chevron down icon Chevron up icon
Next Steps General good advice Path-specific advice Final words Get this book’s PDF version and more
Unlock Your Exclusive Benefits Chevron down icon Chevron up icon
Unlock Your Exclusive Benefits Unlock this book’s free benefits in 3 easy steps
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Index Chevron down icon Chevron up icon
Index
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LLM Engineer's Handbook
LLM Engineer's Handbook
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Full star icon Full star icon Full star icon Full star icon Half star icon 4.8
By Paul Iusztin
October 2024 | 522 pages
Icon Build and refine LLMs step by step, covering data preparation, RAG, and fine-tuning
Icon Learn essential skills for deploying and monitoring LLMs, ensuring optimal performance in production
Icon Utilize preference alignment, evaluation, and inference optimization to enhance performance and adaptability of your LLM applications
Understanding the LLM Twin Concept and Architecture Chevron down icon Chevron up icon
Understanding the LLM Twin Concept and Architecture Understanding the LLM Twin concept Planning the MVP of the LLM Twin product Building ML systems with feature/training/inference pipelines Designing the system architecture of the LLM Twin Summary References
Tooling and Installation Chevron down icon Chevron up icon
Tooling and Installation Python ecosystem and project installation MLOps and LLMOps tooling Databases for storing unstructured and vector data Preparing for AWS Summary References Join our book’s Discord space
Data Engineering Chevron down icon Chevron up icon
Data Engineering Designing the LLM Twin’s data collection pipeline Gathering raw data into the data warehouse Summary References
RAG Feature Pipeline Chevron down icon Chevron up icon
RAG Feature Pipeline Understanding RAG An overview of advanced RAG Exploring the LLM Twin’s RAG feature pipeline architecture Implementing the LLM Twin’s RAG feature pipeline Summary References Join our book’s Discord space
Supervised Fine-Tuning Chevron down icon Chevron up icon
Supervised Fine-Tuning Creating an instruction dataset Creating our own instruction dataset Exploring SFT and its techniques Fine-tuning in practice Summary References
Fine-Tuning with Preference Alignment Chevron down icon Chevron up icon
Fine-Tuning with Preference Alignment Understanding preference datasets Creating our own preference dataset Preference alignment Implementing DPO Summary References Join our book’s Discord space
Evaluating LLMs Chevron down icon Chevron up icon
Evaluating LLMs Model evaluation RAG evaluation Evaluating TwinLlama-3.1-8B Summary References
Inference Optimization Chevron down icon Chevron up icon
Inference Optimization Model optimization strategies Model parallelism Model quantization Summary References Join our book’s Discord space
RAG Inference Pipeline Chevron down icon Chevron up icon
RAG Inference Pipeline Understanding the LLM Twin’s RAG inference pipeline Exploring the LLM Twin’s advanced RAG techniques Implementing the LLM Twin’s RAG inference pipeline Summary References
Inference Pipeline Deployment Chevron down icon Chevron up icon
Inference Pipeline Deployment Criteria for choosing deployment types Understanding inference deployment types Monolithic versus microservices architecture in model serving Exploring the LLM Twin’s inference pipeline deployment strategy Deploying the LLM Twin service Autoscaling capabilities to handle spikes in usage Summary References Join our book’s Discord space
MLOps and LLMOps Chevron down icon Chevron up icon
MLOps and LLMOps The path to LLMOps: Understanding its roots in DevOps and MLOps Deploying the LLM Twin’s pipelines to the cloud Adding LLMOps to the LLM Twin Summary References
MLOps Principles Chevron down icon Chevron up icon
MLOps Principles 1. Automation or operationalization 2. Versioning 3. Experiment tracking 4. Testing 5. Monitoring 6. Reproducibility
Other Books You May Enjoy Chevron down icon Chevron up icon
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Index
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Building AI Agents with LLMs, RAG, and Knowledge Graphs
Building AI Agents with LLMs, RAG, and Knowledge Graphs
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Full star icon Full star icon Full star icon Full star icon Half star icon 4.1
By Salvatore Raieli
July 2025 | 566 pages
Icon Implement RAG and knowledge graphs for advanced problem-solving
Icon Leverage innovative approaches like LangChain to create real-world intelligent systems
Icon Integrate large language models, graph databases, and tool use for next-gen AI solutions
Part 1: The AI Agent Engine: From Text to Large Language Models Chevron down icon Chevron up icon
Part 1: The AI Agent Engine: From Text to Large Language Models
Chapter 1: Analyzing Text Data with Deep Learning Chevron down icon Chevron up icon
Chapter 1: Analyzing Text Data with Deep Learning Technical requirements Representing text for AI Embedding, application, and representation RNNs, LSTMs, GRUs, and CNNs for text Performing sentiment analysis with embedding and deep learning Summary
Chapter 2: The Transformer: The Model Behind the Modern AI Revolution Chevron down icon Chevron up icon
Chapter 2: The Transformer: The Model Behind the Modern AI Revolution Technical requirements Exploring attention and self-attention Introducing the transformer model Training a transformer Exploring masked language modeling Visualizing internal mechanisms Applying a transformer Summary Subscribe for a free eBook
Chapter 3: Exploring LLMs as a Powerful AI Engine Chevron down icon Chevron up icon
Chapter 3: Exploring LLMs as a Powerful AI Engine Technical requirements Discovering the evolution of LLMs Instruction tuning, fine-tuning, and alignment Exploring smaller and more efficient LLMs Exploring multimodal models Understanding hallucinations and ethical and legal issues Prompt engineering Summary Further reading
Part 2: AI Agents and Retrieval of Knowledge Chevron down icon Chevron up icon
Part 2: AI Agents and Retrieval of Knowledge
Chapter 4: Building a Web Scraping Agent with an LLM Chevron down icon Chevron up icon
Chapter 4: Building a Web Scraping Agent with an LLM Technical requirements Understanding the brain, perception, and action paradigm Classifying AI agents Understanding the abilities of single-agent and multiple-agent systems Exploring the principal libraries Creating an agent to search the web Summary Further reading Subscribe for a free eBook
Chapter 5: Extending Your Agent with RAG to Prevent Hallucinations Chevron down icon Chevron up icon
Chapter 5: Extending Your Agent with RAG to Prevent Hallucinations Technical requirements Exploring naïve RAG Retrieval, optimization, and augmentation Evaluating the output Comparison between RAG and fine-tuning Using RAG to build a movie recommendation agent Summary Further reading
Chapter 6: Advanced RAG Techniques for Information Retrieval and Augmentation Chevron down icon Chevron up icon
Chapter 6: Advanced RAG Techniques for Information Retrieval and Augmentation Technical requirements Discussing naïve RAG issues Exploring the advanced RAG pipeline Modular RAG and its integration with other systems Implementing an advanced RAG pipeline Understanding the scalability and performance of RAG Open questions and future perspectives Summary Further reading Subscribe for a free eBook
Chapter 7: Creating and Connecting a Knowledge Graph to an AI Agent Chevron down icon Chevron up icon
Chapter 7: Creating and Connecting a Knowledge Graph to an AI Agent Technical requirements Introduction to knowledge graphs Creating a knowledge graph with your LLM Retrieving information with a knowledge graph and an LLM Understanding graph reasoning Ongoing challenges in knowledge graphs and GraphRAG Summary Further reading
Chapter 8: Reinforcement Learning and AI Agents Chevron down icon Chevron up icon
Chapter 8: Reinforcement Learning and AI Agents Technical requirements Introduction to reinforcement learning Deep reinforcement learning LLM interactions with RL models Key takeaways Summary Further reading Subscribe for a free eBook
Part 3: Creating Sophisticated AI to Solve Complex Scenarios Chevron down icon Chevron up icon
Part 3: Creating Sophisticated AI to Solve Complex Scenarios
Chapter 9: Creating Single- and Multi-Agent Systems Chevron down icon Chevron up icon
Chapter 9: Creating Single- and Multi-Agent Systems Technical requirements Introduction to autonomous agents Working with HuggingGPT Multi-agent system SaaS, MaaS, DaaS, and RaaS Summary Further reading
Chapter 10: Building an AI Agent Application Chevron down icon Chevron up icon
Chapter 10: Building an AI Agent Application Technical requirements Introduction to Streamlit Developing our frontend with Streamlit Creating an application with Streamlit and AI agents Machine learning operations and LLM operations Asynchronous programming Docker Summary Further reading Subscribe for a free eBook
Chapter 11: The Future Ahead Chevron down icon Chevron up icon
Chapter 11: The Future Ahead AI agents in healthcare AI agents in other sectors Challenges and open questions Summary Further reading
Chapter 12: Unlock Your Exclusive Benefits Chevron down icon Chevron up icon
Chapter 12: Unlock Your Exclusive Benefits Unlock this Book’s Free Benefits in 3 Easy Steps
Index Chevron down icon Chevron up icon
Index Why subscribe?
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Mastering QuickBooks 2026
Mastering QuickBooks 2026
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By Crystalynn Shelton
December 2025 | 544 pages
Icon Be the first to explore AI-powered QuickBooks workflows, including Intuit Assist and AI Agents
Icon Apply learning through real-world case studies across retail, services, and property management
Icon Reinforce your knowledge with a companion workbook and exclusive video tutorials on bookkeeping and financial reporting
Icon Free with your book: PDF Copy, AI Assistant, and Next-Gen Reader
Part 1: Setting Up Your Company File Chevron down icon Chevron up icon
Part 1: Setting Up Your Company File
Small-Business Bookkeeping 101 Chevron down icon Chevron up icon
Small-Business Bookkeeping 101 Free Benefits with Your Book Recording sales Recording expenses Recording inventory and fixed-asset purchases Recording liabilities The chart of accounts Choosing an accounting method Double-entry bookkeeping Financial statements Summary Next steps
Getting Started with QuickBooks Online Chevron down icon Chevron up icon
Getting Started with QuickBooks Online What is QuickBooks? Understanding the role of AI agents in QBO Exploring QBO editions Choosing the right QBO edition Creating a QBO account Navigating in QBO Summary Next steps
Company File Setup Chevron down icon Chevron up icon
Company File Setup Key information and documents Setting up company preferences in QBO Summary Next steps
Customizing QuickBooks for Your Business Chevron down icon Chevron up icon
Customizing QuickBooks for Your Business Customizing the chart of accounts list Connecting bank accounts to QBO Connecting credit card accounts to QBO Giving other users access to your QuickBooks data Using apps in QBO Customizing sales templates Summary Next steps
Managing Customer and Vendor Lists Chevron down icon Chevron up icon
Managing Customer and Vendor Lists Managing customer lists in QBO Managing vendor lists in QBO Summary Next steps
Part 2: Recording Transactions in QBO Chevron down icon Chevron up icon
Part 2: Recording Transactions in QBO
Managing Sales Tax Chevron down icon Chevron up icon
Managing Sales Tax Do you need to charge sales tax? Setting up sales tax in QBO Creating an invoice that includes sales tax Sales tax reports Paying sales tax when it comes due Summary
Recording Sales Transactions in QuickBooks Online Chevron down icon Chevron up icon
Recording Sales Transactions in QuickBooks Online Recording sales Recording customer payments manually Issuing credit memos and refunds to customers Creating estimates in QBO Summary
Customer Sales Reports in QuickBooks Online Chevron down icon Chevron up icon
Customer Sales Reports in QuickBooks Online Generating an accounts receivable aging report Generating an open invoices report Generating a sales by customer report Generating a sales by product/service report Generating a Sales by Class Summary report Summary Next steps
Recording Expenses in QuickBooks Online Chevron down icon Chevron up icon
Recording Expenses in QuickBooks Online Adding and paying bills Entering bills into QuickBooks Online Uploading bills to QuickBooks Paying bills in QuickBooks Entering vendor credits into QuickBooks Managing recurring expenses Writing checks Printing checks Editing, voiding, and deleting expenses Capturing and categorizing receipts and bills Tracking mileage Summary Next steps
Vendor and Expenses Reports in QuickBooks Online Chevron down icon Chevron up icon
Vendor and Expenses Reports in QuickBooks Online Understanding the accounts payable (A/P) aging report Utilizing the unpaid bills report Understanding the expenses-by-vendor summary report Utilizing the bill payments report Summary Next steps
Part 3: Managing Bank Feeds, EE’s, and Contractors in QBO Chevron down icon Chevron up icon
Part 3: Managing Bank Feeds, EE’s, and Contractors in QBO
Managing Bank Feeds and Reconciling Bank and Credit Card Accounts Chevron down icon Chevron up icon
Managing Bank Feeds and Reconciling Bank and Credit Card Accounts Overview of the Banking Center Matching transactions Editing banking transactions Understanding bank rules Reconciling accounts Troubleshooting tips for reconciling bank and credit card accounts Summary Next steps
Managing 1099 Contractors and Introducing QuickBooks Time in QBO Chevron down icon Chevron up icon
Managing 1099 Contractors and Introducing QuickBooks Time in QBO Setting up 1099 contractors Tracking and paying 1099 contractors 1099 year-end reporting Introducing QuickBooks Time Summary Next steps
Managing Employees in QuickBooks Online Chevron down icon Chevron up icon
Managing Employees in QuickBooks Online Setting up payroll Adding new employees to payroll Generating payroll reports Filing payroll tax forms and payments Summary Next steps
Part 4: Closing the Books and Generating Reports in QBO Chevron down icon Chevron up icon
Part 4: Closing the Books and Generating Reports in QBO
Report Center Overview Chevron down icon Chevron up icon
Report Center Overview Navigating the Reports Center Reports available in the Reports Center Customizing reports Exporting reports Summary Next steps
Business Overview and Cash Management Tools and Reports Chevron down icon Chevron up icon
Business Overview and Cash Management Tools and Reports Understanding profit and loss statements Generating balance sheet reports Understanding the statement of cash flows Creating a budget Using the Cash Flow Center Generating the audit log report Summary Next steps
Closing the Books in QuickBooks Online Chevron down icon Chevron up icon
Closing the Books in QuickBooks Online Recording journal entries Reviewing a checklist for closing your books Summary Next steps
Part 5: Integrating E-Commerce Platforms and Advanced Inventory Chevron down icon Chevron up icon
Part 5: Integrating E-Commerce Platforms and Advanced Inventory
Integrating E-Commerce Platforms with QuickBooks Online Chevron down icon Chevron up icon
Integrating E-Commerce Platforms with QuickBooks Online Reviewing integrations before connecting them to QBO Pros of connecting a sales channel to QuickBooks Cons of connecting a sales channel to QuickBooks How to connect a sales channel to QuickBooks Summary Next steps
Managing Products and Services Chevron down icon Chevron up icon
Managing Products and Services Turning on inventory tracking in QuickBooks Adding products in QuickBooks Online Recording product sales in QuickBooks Online Ordering products from vendor suppliers in QuickBooks Receiving products into the inventory Generating inventory reports Summary Next steps
Unlock this Book’s Free Benefits in 3 Easy Steps Chevron down icon Chevron up icon
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Index
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C# 14 and .NET 10 – Modern Cross-Platform Development Fundamentals
C# 14 and .NET 10 – Modern Cross-Platform Development Fundamentals
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Full star icon Full star icon Full star icon Full star icon Half star icon 4.3
By Mark J. Price
November 2025 | 828 pages
Icon Explore the newest additions to the C# language, the .NET class libraries, and data modeling with Entity Framework Core
Icon Build professional modern websites and services with ASP.NET Core, Blazor, and Minimal API services
Icon Enhance your skills with step-by-step code examples and good practice tips
Hello, C#! Welcome, .NET! Chevron down icon Chevron up icon
Hello, C#! Welcome, .NET! Free Benefits with Your Book Introducing this book and its contents Setting up your development environment Understanding .NET Building console apps and managing multiple projects in a solution Making good use of the GitHub repository for this book Running a C# code file without a project file Looking for help Practicing and exploring Summary Join .NETPro — It’s Free
Speaking C# Chevron down icon Chevron up icon
Speaking C# Introducing the C# language Discovering your C# compiler version Understanding C# grammar and vocabulary Working with variables Exploring more about console apps Practicing and exploring Summary
Controlling Flow, Converting Types, and Handling Exceptions Chevron down icon Chevron up icon
Controlling Flow, Converting Types, and Handling Exceptions Operating on variables Understanding selection statements Understanding iteration statements Storing multiple values in an array Casting and converting between types Handling exceptions Checking for overflow Practicing and exploring Summary
Writing, Debugging, and Testing Functions Chevron down icon Chevron up icon
Writing, Debugging, and Testing Functions Writing functions Debugging during development Hot reloading during development Unit testing Throwing and catching exceptions in functions Practicing and exploring Summary Join .NETPro — It’s Free
Building Your Own Types with Object-Oriented Programming Chevron down icon Chevron up icon
Building Your Own Types with Object-Oriented Programming Talking about OOP Building class libraries Storing data in fields Working with methods and tuples Controlling access with properties and indexers Pattern matching with objects Working with record types Practicing and exploring Summary
Implementing Interfaces and Inheriting Classes Chevron down icon Chevron up icon
Implementing Interfaces and Inheriting Classes Setting up a class library and console app Static methods and overloading operators Making types safely reusable with generics Raising and handling events Implementing interfaces Working with null values Inheriting from classes Casting within inheritance hierarchies Inheriting and extending .NET types Practicing and exploring Summary
Packaging and Distributing .NET Types Chevron down icon Chevron up icon
Packaging and Distributing .NET Types The road to .NET 10 Understanding .NET components Publishing your code for deployment Native ahead-of-time compilation Packaging your libraries for NuGet distribution Practicing and exploring Summary
Working with Common .NET Types Chevron down icon Chevron up icon
Working with Common .NET Types Working with numbers Working with text Pattern matching with regular expressions Storing multiple objects in collections Practicing and exploring Summary Join .NETPro — It’s Free
Working with Files, Streams, and Serialization Chevron down icon Chevron up icon
Working with Files, Streams, and Serialization Managing a filesystem Reading and writing with streams Encoding and decoding text Serializing object graphs Practicing and exploring Summary
Working with Data Using Entity Framework Core Chevron down icon Chevron up icon
Working with Data Using Entity Framework Core Understanding modern databases Setting up EF Core in a .NET project Defining EF Core models Querying EF Core models Practicing and exploring Summary
Querying and Manipulating Data Using LINQ Chevron down icon Chevron up icon
Querying and Manipulating Data Using LINQ Writing LINQ expressions LINQ in practice Sorting and more Using LINQ with EF Core Joining, grouping, and lookups Practicing and exploring Summary
Introducing Modern Web Development Using .NET Chevron down icon Chevron up icon
Introducing Modern Web Development Using .NET Understanding ASP.NET Core Structuring projects Building an entity model for use in the rest of the book Practicing and exploring Summary Join .NETPro — It’s Free
Building Websites Using ASP.NET Core Chevron down icon Chevron up icon
Building Websites Using ASP.NET Core Exploring ASP.NET Core History of Blazor Exploring Blazor static SSR Using EF Core with ASP.NET Core Practicing and exploring Summary
Building Interactive Web Components Using Blazor Chevron down icon Chevron up icon
Building Interactive Web Components Using Blazor Reviewing the Blazor Web App project template Building components using Blazor Defining forms using the EditForm component Practicing and exploring Summary
Building and Consuming Web Services Chevron down icon Chevron up icon
Building and Consuming Web Services Building web services using ASP.NET Core Creating a web service for the Northwind database Documenting and trying out web services Consuming web services using HTTP clients Practicing and exploring Summary
Unlock Your Exclusive Benefits Chevron down icon Chevron up icon
Unlock Your Exclusive Benefits Unlock this Book’s Free Benefits in 3 Easy Steps
Epilogue Chevron down icon Chevron up icon
Epilogue Next steps on your C# and .NET learning journey Using this book with future versions of .NET Good luck!
Index Chevron down icon Chevron up icon
Index
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Bare-Metal Embedded C Programming
Bare-Metal Embedded C Programming
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Full star icon Full star icon Full star icon Full star icon Full star icon 5
By Israel Gbati
September 2024 | 448 pages
Icon Learn to develop bare-metal firmware for Arm microcontrollers from scratch
Icon Understand hardware intricacies to minimize your dependency on third-party libraries
Icon Navigate microcontroller manuals with ease and learn to write optimized code
Chapter 1: Setting Up the Tools of the Trade Chevron down icon Chevron up icon
Chapter 1: Setting Up the Tools of the Trade Getting the most out of this book – get to know your free benefits Technical requirements Essential development tools for microcontrollers The development board Datasheets and manuals – unraveling the details Navigating the STM32CubeIDE Understanding the control icons Summary
Chapter 2: Constructing Peripheral Registers from Memory Addresses Chevron down icon Chevron up icon
Chapter 2: Constructing Peripheral Registers from Memory Addresses Technical requirements The different types of firmware development Locating and understanding the development board’s components Defining and creating registers through documentation insights Register manipulation – from configuration to running your first firmware Summary
Chapter 3: Understanding the Build Process and Exploring the GNU Toolchain Chevron down icon Chevron up icon
Chapter 3: Understanding the Build Process and Exploring the GNU Toolchain Technical requirements The foundations – understanding the embedded build process A tour of GNU binary tools for embedded systems From IDE to the command line – watching the build process unfold Summary
Chapter 4: Developing the Linker Script and Startup File Chevron down icon Chevron up icon
Chapter 4: Developing the Linker Script and Startup File Technical requirements Understanding the STM32 memory model The linker script Writing the linker script and startup file Summary
Chapter 5: The “Make” Build System Chevron down icon Chevron up icon
Chapter 5: The “Make” Build System Technical requirements An introduction to build systems The Make build system Writing Makefiles for firmware projects Summary
Chapter 6: The Common Microcontroller Software Interface Standard (CMSIS) Chevron down icon Chevron up icon
Chapter 6: The Common Microcontroller Software Interface Standard (CMSIS) Technical requirements Defining peripheral registers with C structures Understanding CMSIS Setting up the required CMSIS files Summary
Chapter 7: The General-Purpose Input/Output (GPIO) Peripheral Chevron down icon Chevron up icon
Chapter 7: The General-Purpose Input/Output (GPIO) Peripheral Technical requirements Understanding the GPIO peripheral The STM32 GPIO registers Developing input and output drivers Summary
Chapter 8: System Tick (SysTick) Timer Chevron down icon Chevron up icon
Chapter 8: System Tick (SysTick) Timer Technical requirements Introduction to the SysTick timer Developing a driver for the SysTick timer Summary
Chapter 9: General-Purpose Timers (TIM) Chevron down icon Chevron up icon
Chapter 9: General-Purpose Timers (TIM) Technical requirements Introduction to timers and their uses Common use cases of timers STM32 timers Developing the timer driver Summary
Chapter 10: The Universal Asynchronous Receiver/Transmitter Protocol Chevron down icon Chevron up icon
Chapter 10: The Universal Asynchronous Receiver/Transmitter Protocol Technical requirements Introduction to communication protocols Overview of the UART protocol Developing the UART driver Summary
Chapter 11: Analog-to-Digital Converter (ADC) Chevron down icon Chevron up icon
Chapter 11: Analog-to-Digital Converter (ADC) Technical requirements Overview of analog-to-digital conversion The STM32F4 ADC peripheral The key ADC registers and flags Developing the ADC driver Summary
Chapter 12: Serial Peripheral Interface (SPI) Chevron down icon Chevron up icon
Chapter 12: Serial Peripheral Interface (SPI) Technical requirements Overview of the SPI protocol The STM32F4 SPI peripherals Developing the SPI driver Summary
Chapter 13: Inter-Integrated Circuit (I2C) Chevron down icon Chevron up icon
Chapter 13: Inter-Integrated Circuit (I2C) Technical requirements An overview of the I2C protocol The STM32F4 I2C peripherals Developing the I2C driver Summary
Chapter 14: External Interrupts and Events (EXTI) Chevron down icon Chevron up icon
Chapter 14: External Interrupts and Events (EXTI) Technical requirements Interrupts and their role in firmware The STM32 EXTI controller Developing the EXTI driver Summary
Chapter 15: The Real-Time Clock (RTC) Chevron down icon Chevron up icon
Chapter 15: The Real-Time Clock (RTC) Technical requirements Understanding RTCs The STM32 RTC module Some key RTC registers Developing the RTC driver Summary
Chapter 16: Independent Watchdog (IWDG) Chevron down icon Chevron up icon
Chapter 16: Independent Watchdog (IWDG) Technical requirements Understanding WDTs The STM32 IWDG Developing the IWDG driver Summary
Chapter 17: Direct Memory Access (DMA) Chevron down icon Chevron up icon
Chapter 17: Direct Memory Access (DMA) Technical requirements Understanding Direct Memory Access (DMA) The DMA modules of the STM32F4 microcontroller Developing the ADC DMA driver Developing the UART DMA driver Developing the DMA memory-to-memory driver Summary
Chapter 18: Power Management and Energy Efficiency in Embedded Systems Chevron down icon Chevron up icon
Chapter 18: Power Management and Energy Efficiency in Embedded Systems Technical requirements An overview of power management techniques Wake-up sources and triggers from low-power modes in STM32F4 Developing a driver to enter standby mode and wake up
Chapter 19: Unlock Your Book’s Exclusive Benefits Chevron down icon Chevron up icon
Chapter 19: Unlock Your Book’s Exclusive Benefits How to unlock these benefits in three easy steps
Index Chevron down icon Chevron up icon
Index Why subscribe?
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Practical C# Projects with .NET
Practical C# Projects with .NET
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By Matt Eland
April 2026 | 518 pages
Icon Learn by building practical .NET applications for real-world use
Icon Develop cross-platform apps, games, web APIs, and AI integrations
Icon Master new C# and .NET features through creative, hands-on projects
Part 1: Building Console Applications Chevron down icon Chevron up icon
Part 1: Building Console Applications
Chapter 1: Building an Adventure Game Console App with .NET and Spectre.Console Chevron down icon Chevron up icon
Chapter 1: Building an Adventure Game Console App with .NET and Spectre.Console Technical requirements Building a simple prototype Scaling our prototype with object-oriented programming Linking our objects together Improving input and output with Spectre.Console Hardening our application with exception handling and tests Journeying onwards: where to go from here Summary Further reading Subscribe to Deep Engineering
Chapter 2: Recreating the Enigma Machine with Object-Oriented Programming Chevron down icon Chevron up icon
Chapter 2: Recreating the Enigma Machine with Object-Oriented Programming Technical requirements Understanding cryptography and the Enigma machine Designing and structuring our Enigma machine solution Implementing an early solution guided by tests Connecting Enigma using the Chain of Responsibility pattern Managing complexity with xUnit tests Parsing command-line arguments with Spectre.Console command apps Journeying onwards: where to go from here Summary Further reading
Chapter 3: Exploring Infinite Worlds with Procedural Generation Chevron down icon Chevron up icon
Chapter 3: Exploring Infinite Worlds with Procedural Generation Technical requirements Designing a role-playing game Generating infinite worlds with Perlin Noise Laying out the overworld map user interface Building the main loop and screen manager Managing game state with the GameManager Unit testing Spectre.Console applications Journeying onwards – where to go from here Summary Further reading Subscribe to Deep Engineering
Chapter 4: Building a Data-Driven Role-Playing Game Chevron down icon Chevron up icon
Chapter 4: Building a Data-Driven Role-Playing Game Technical requirements Expanding our game to include new screens Loading enemy encounters from JSON files Supporting real-time battles Modularizing logic with the strategy pattern Journeying onward: where to go from here Summary Further reading
Part 2: Web, Mobile, and Desktop Applications Chevron down icon Chevron up icon
Part 2: Web, Mobile, and Desktop Applications
Chapter 5: Tracking Collectible Card Games with ASP.NET Minimal APIs and Entity Framework Chevron down icon Chevron up icon
Chapter 5: Tracking Collectible Card Games with ASP.NET Minimal APIs and Entity Framework Technical requirements Designing our project's data model Defining the data model with Entity Framework Working with hierarchies in EF Using ASP.NET minimal APIs Mapping with AutoMapper Expanding our API and supporting the REPR pattern Implementing JWT authentication Journeying onward: where to go from here Summary Further reading Subscribe to Deep Engineering
Chapter 6: Creating a Trading Card Tracker Using Blazor WebAssembly Chevron down icon Chevron up icon
Chapter 6: Creating a Trading Card Tracker Using Blazor WebAssembly Technical requirements Understanding Blazor and WebAssembly (WASM) Connecting our Blazor client to a Web API Creating a card list view with Bootstrap Handling login and authentication Viewing and creating decks Updating decks and saving changes Journeying onwards: where to go from here Summary Further reading
Chapter 7: Building a Cross-Platform Chatbot with Uno Platform and ELIZA Chevron down icon Chevron up icon
Chapter 7: Building a Cross-Platform Chatbot with Uno Platform and ELIZA Technical requirements Conversing with ELIZA Getting started with Uno Platform Creating our view Defining app data with Model-View-Update-eXtended Building a chat client with Microsoft.Extensions.AI Journeying onward: where to go from here Summary Further reading Subscribe to Deep Engineering
Part 3: Building AI-powered side projects Chevron down icon Chevron up icon
Part 3: Building AI-powered side projects
Chapter 8: Building Conversational AI Partners with Ollama and Microsoft.Extensions.AI Chevron down icon Chevron up icon
Chapter 8: Building Conversational AI Partners with Ollama and Microsoft.Extensions.AI Technical requirements Understanding conversational AI systems Installing local LLMs with Ollama Telling jokes with Ollama chat partners Building a haiku bot and tuning its behavior Adding function calling to our bot Journeying onward: where to go from here Summary Further reading
Chapter 9: Building an AI Librarian with Microsoft Agent Framework Chevron down icon Chevron up icon
Chapter 9: Building an AI Librarian with Microsoft Agent Framework Technical requirements Understanding AI orchestration applications Exploring our application Indexing documents with custom embeddings Searching embeddings with cosine similarity Building Agent Framework chat handlers Integrating document search into an AI agent Journeying onwards Summary Further reading Subscribe to Deep Engineering
Chapter 10: Building and Monitoring a Model Context Protocol Server with Aspire Chevron down icon Chevron up icon
Chapter 10: Building and Monitoring a Model Context Protocol Server with Aspire Technical requirements Understanding Model Context Protocol Orchestrating applications with Aspire Building a Model Context Protocol server with SSE Observing AI applications with OpenTelemetry Building a Blazor chat interface with MudBlazor Connecting Agent Framework to a MCP server Extending our Model Context Protocol server Journeying onwards Summary Further reading
Chapter 11: Predicting Values with Machine Learning, IoT Data, and ML.NET Chevron down icon Chevron up icon
Chapter 11: Predicting Values with Machine Learning, IoT Data, and ML.NET Technical requirements Understanding machine learning and ML.NET Selecting a dataset Loading and splitting training data Training regression models with AutoML Evaluating regression models Predicting future values Evaluating the impact of different conditions Saving and loading models Journeying onward Summary Further reading Subscribe to Deep Engineering
Part 4: Cross-platform Game Development with MonoGame Chevron down icon Chevron up icon
Part 4: Cross-platform Game Development with MonoGame
Chapter 12: Recreating Pong in MonoGame with C# Chevron down icon Chevron up icon
Chapter 12: Recreating Pong in MonoGame with C# Technical requirements Understanding the .NET game development landscape Recreating Pong with MonoGame Initializing our game Maintaining game logic in the Update step Drawing objects to the screen Writing text to the screen with SpriteFonts Journeying onward Summary Further reading
Chapter 13: Building Larger Games with Graphics, Collisions, and AI Chevron down icon Chevron up icon
Chapter 13: Building Larger Games with Graphics, Collisions, and AI Technical requirements Building our SpaceGame Defining game entities Managing game entities Loading sprites, texture atlases, and sprite sheets Drawing the background and animations Controlling enemies using behavior trees Journeying onwards Summary Further reading Subscribe to Deep Engineering
Chapter 14: Succeeding with Side Projects Chevron down icon Chevron up icon
Chapter 14: Succeeding with Side Projects Understanding the value of side projects Selecting a side project Scoping your side project Starting side projects Making progress on side projects Concluding side projects Further reading
Chapter 15: Unlock Your Exclusive Benefits Chevron down icon Chevron up icon
Chapter 15: Unlock Your Exclusive Benefits Unlock this Book's Free Benefits in 3 Easy Steps
Index Chevron down icon Chevron up icon
Index
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Building Agentic AI Systems
Building Agentic AI Systems
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Full star icon Full star icon Full star icon Half star icon Empty star icon 3.8
By Anjanava Biswas
April 2025 | 292 pages
Icon Understand the foundations and advanced techniques of building intelligent, autonomous AI agents
Icon Learn advanced techniques for reflection, introspection, tool use, planning, and collaboration in agentic systems
Icon Explore crucial aspects of trust, safety, and ethics in AI agent development and applications
Part 1: Foundations of Generative AI and Agentic Systems Chevron down icon Chevron up icon
Part 1: Foundations of Generative AI and Agentic Systems
Chapter 1: Fundamentals of Generative AI Chevron down icon Chevron up icon
Chapter 1: Fundamentals of Generative AI Introduction to generative AI Types of generative AI models Applications of generative AI Challenges and limitations of generative AI Summary Questions Answers Further reading References
Chapter 2: Principles of Agentic Systems Chevron down icon Chevron up icon
Chapter 2: Principles of Agentic Systems Technical requirements Understanding self-governance, agency, and autonomy Reviewing intelligent agents and their characteristics Exploring the architecture of agentic systems Understanding multi-agent systems Summary Questions Answers Subscribe to AI_Distilled, the go-to newsletter for AI professionals
Chapter 3: Essential Components of Intelligent Agents Chevron down icon Chevron up icon
Chapter 3: Essential Components of Intelligent Agents Technical requirements Knowledge representation in intelligent agents Reasoning in intelligent agents Learning mechanisms for adaptive agents Decision-making and planning in agentic systems Enhancing agent capabilities with generative AI Summary Questions Answers
Part 2: Designing and Implementing Generative AI-Based Agents Chevron down icon Chevron up icon
Part 2: Designing and Implementing Generative AI-Based Agents
Chapter 4: Reflection and Introspection in Agents Chevron down icon Chevron up icon
Chapter 4: Reflection and Introspection in Agents Technical requirements The importance of reflection in agents Introspection in intelligent agents Implementing reflective capabilities Use cases and examples Summary Questions Answers Subscribe to AI_Distilled, the go-to newsletter for AI professionals
Chapter 5: Enabling Tool Use and Planning in Agents Chevron down icon Chevron up icon
Chapter 5: Enabling Tool Use and Planning in Agents Technical requirements Understanding the concept of tool use in agents Planning algorithms for agents Integrating tool use and planning Exploring practical implementations Summary Questions Answers
Chapter 6: Exploring the Coordinator, Worker, and Delegator Approach Chevron down icon Chevron up icon
Chapter 6: Exploring the Coordinator, Worker, and Delegator Approach Technical requirements Understanding the CWD model Designing agents with role assignments Communication and collaboration between agents Implementing the CWD approach in generative AI systems Summary Questions Answers Subscribe to AI_Distilled, the go-to newsletter for AI professionals
Chapter 7: Effective Agentic System Design Techniques Chevron down icon Chevron up icon
Chapter 7: Effective Agentic System Design Techniques Technical requirements Focused system prompts and instructions for agents State spaces and environment modeling Agent memory architecture and context management Sequential and parallel processing in agentic workflows Summary Questions Answers
Part 3: Trust, Safety, Ethics, and Applications Chevron down icon Chevron up icon
Part 3: Trust, Safety, Ethics, and Applications
Chapter 8: Building Trust in Generative AI Systems Chevron down icon Chevron up icon
Chapter 8: Building Trust in Generative AI Systems Technical requirements Importance of trust in AI Techniques for establishing trust Implementing transparency and explainability Handling uncertainty and biases Summary Questions Answers Subscribe to AI_Distilled, the go-to newsletter for AI professionals
Chapter 9: Managing Safety and Ethical Considerations Chevron down icon Chevron up icon
Chapter 9: Managing Safety and Ethical Considerations Understanding potential risks and challenges Ensuring safe and responsible AI Exploring ethical guidelines and frameworks Addressing privacy and security concerns Summary Questions Answers
Chapter 10: Common Use Cases and Applications Chevron down icon Chevron up icon
Chapter 10: Common Use Cases and Applications Creative and artistic applications Natural language processing and conversational agents Robotics and autonomous systems Decision support and optimization Summary Questions Answers Subscribe to AI_Distilled, the go-to newsletter for AI professionals
Chapter 11: Conclusion and Future Outlook Chevron down icon Chevron up icon
Chapter 11: Conclusion and Future Outlook Recap of key concepts Emerging trends and research directions Artificial general intelligence Challenges and opportunities Summary
Chapter 12: Unlock Your Exclusive Benefits Chevron down icon Chevron up icon
Chapter 12: Unlock Your Exclusive Benefits Unlock this Book’s Free Benefits in 3 Easy Steps
Index Chevron down icon Chevron up icon
Index Why subscribe?
Other Books You May Enjoy Chevron down icon Chevron up icon
Other Books You May Enjoy Packt is searching for authors like you Share Your Thoughts Join our community on Discord Subscribe to AI_Distilled, the go-to newsletter for AI professionals
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Machine Learning with PyTorch and Scikit-Learn
Machine Learning with PyTorch and Scikit-Learn
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Full star icon Full star icon Full star icon Full star icon Half star icon 4.4
By Sebastian Raschka
February 2022 | 774 pages
Icon Learn applied machine learning with a solid foundation in theory
Icon Clear, intuitive explanations take you deep into the theory and practice of Python machine learning
Icon Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices
Giving Computers the Ability to Learn from Data Chevron down icon Chevron up icon
Giving Computers the Ability to Learn from Data Building intelligent machines to transform data into knowledge The three different types of machine learning Introduction to the basic terminology and notations A roadmap for building machine learning systems Using Python for machine learning Summary
Training Simple Machine Learning Algorithms for Classification Chevron down icon Chevron up icon
Training Simple Machine Learning Algorithms for Classification Artificial neurons – a brief glimpse into the early history of machine learning Implementing a perceptron learning algorithm in Python Adaptive linear neurons and the convergence of learning Summary
A Tour of Machine Learning Classifiers Using Scikit-Learn Chevron down icon Chevron up icon
A Tour of Machine Learning Classifiers Using Scikit-Learn Choosing a classification algorithm First steps with scikit-learn – training a perceptron Modeling class probabilities via logistic regression Maximum margin classification with support vector machines Solving nonlinear problems using a kernel SVM Decision tree learning K-nearest neighbors – a lazy learning algorithm Summary
Building Good Training Datasets – Data Preprocessing Chevron down icon Chevron up icon
Building Good Training Datasets – Data Preprocessing Dealing with missing data Handling categorical data Partitioning a dataset into separate training and test datasets Bringing features onto the same scale Selecting meaningful features Assessing feature importance with random forests Summary
Compressing Data via Dimensionality Reduction Chevron down icon Chevron up icon
Compressing Data via Dimensionality Reduction Unsupervised dimensionality reduction via principal component analysis Supervised data compression via linear discriminant analysis Nonlinear dimensionality reduction and visualization Summary
Learning Best Practices for Model Evaluation and Hyperparameter Tuning Chevron down icon Chevron up icon
Learning Best Practices for Model Evaluation and Hyperparameter Tuning Streamlining workflows with pipelines Using k-fold cross-validation to assess model performance Debugging algorithms with learning and validation curves Fine-tuning machine learning models via grid search Looking at different performance evaluation metrics Summary
Combining Different Models for Ensemble Learning Chevron down icon Chevron up icon
Combining Different Models for Ensemble Learning Learning with ensembles Combining classifiers via majority vote Bagging – building an ensemble of classifiers from bootstrap samples Leveraging weak learners via adaptive boosting Gradient boosting – training an ensemble based on loss gradients Summary
Applying Machine Learning to Sentiment Analysis Chevron down icon Chevron up icon
Applying Machine Learning to Sentiment Analysis Preparing the IMDb movie review data for text processing Introducing the bag-of-words model Training a logistic regression model for document classification Working with bigger data – online algorithms and out-of-core learning Topic modeling with latent Dirichlet allocation Summary
Predicting Continuous Target Variables with Regression Analysis Chevron down icon Chevron up icon
Predicting Continuous Target Variables with Regression Analysis Introducing linear regression Exploring the Ames Housing dataset Implementing an ordinary least squares linear regression model Fitting a robust regression model using RANSAC Evaluating the performance of linear regression models Using regularized methods for regression Turning a linear regression model into a curve – polynomial regression Dealing with nonlinear relationships using random forests Summary
Working with Unlabeled Data – Clustering Analysis Chevron down icon Chevron up icon
Working with Unlabeled Data – Clustering Analysis Grouping objects by similarity using k-means Organizing clusters as a hierarchical tree Locating regions of high density via DBSCAN Summary
Implementing a Multilayer Artificial Neural Network from Scratch Chevron down icon Chevron up icon
Implementing a Multilayer Artificial Neural Network from Scratch Modeling complex functions with artificial neural networks Classifying handwritten digits Training an artificial neural network About convergence in neural networks A few last words about the neural network implementation Summary
Parallelizing Neural Network Training with PyTorch Chevron down icon Chevron up icon
Parallelizing Neural Network Training with PyTorch PyTorch and training performance First steps with PyTorch Building input pipelines in PyTorch Building an NN model in PyTorch Choosing activation functions for multilayer neural networks Summary
Going Deeper – The Mechanics of PyTorch Chevron down icon Chevron up icon
Going Deeper – The Mechanics of PyTorch The key features of PyTorch PyTorch’s computation graphs PyTorch tensor objects for storing and updating model parameters Computing gradients via automatic differentiation Simplifying implementations of common architectures via the torch.nn module Project one – predicting the fuel efficiency of a car Project two – classifying MNIST handwritten digits Higher-level PyTorch APIs: a short introduction to PyTorch-Lightning Summary
Classifying Images with Deep Convolutional Neural Networks Chevron down icon Chevron up icon
Classifying Images with Deep Convolutional Neural Networks The building blocks of CNNs Putting everything together – implementing a CNN Implementing a deep CNN using PyTorch Smile classification from face images using a CNN Summary
Modeling Sequential Data Using Recurrent Neural Networks Chevron down icon Chevron up icon
Modeling Sequential Data Using Recurrent Neural Networks Introducing sequential data RNNs for modeling sequences Implementing RNNs for sequence modeling in PyTorch Summary
Transformers – Improving Natural Language Processing with Attention Mechanisms Chevron down icon Chevron up icon
Transformers – Improving Natural Language Processing with Attention Mechanisms Adding an attention mechanism to RNNs Introducing the self-attention mechanism Attention is all we need: introducing the original transformer architecture Building large-scale language models by leveraging unlabeled data Fine-tuning a BERT model in PyTorch Summary
Generative Adversarial Networks for Synthesizing New Data Chevron down icon Chevron up icon
Generative Adversarial Networks for Synthesizing New Data Introducing generative adversarial networks Implementing a GAN from scratch Improving the quality of synthesized images using a convolutional and Wasserstein GAN Other GAN applications Summary
Graph Neural Networks for Capturing Dependencies in Graph Structured Data Chevron down icon Chevron up icon
Graph Neural Networks for Capturing Dependencies in Graph Structured Data Introduction to graph data Understanding graph convolutions Implementing a GNN in PyTorch from scratch Implementing a GNN using the PyTorch Geometric library Other GNN layers and recent developments Summary
Reinforcement Learning for Decision Making in Complex Environments Chevron down icon Chevron up icon
Reinforcement Learning for Decision Making in Complex Environments Introduction – learning from experience The theoretical foundations of RL Reinforcement learning algorithms Implementing our first RL algorithm A glance at deep Q-learning Chapter and book summary
Other Books You May Enjoy Chevron down icon Chevron up icon
Other Books You May Enjoy
Index Chevron down icon Chevron up icon
Index
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Generative AI with LangChain
Generative AI with LangChain
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Full star icon Full star icon Full star icon Full star icon Half star icon 4.5
By Ben Auffarth
May 2025 | 484 pages
Icon Bridge the gap between prototype and production with robust LangGraph agent architectures
Icon Apply enterprise-grade practices for testing, observability, and monitoring
Icon Build specialized agents for software development and data analysis
Icon Purchase of the print or Kindle book includes a free PDF eBook
The Rise of Generative AI: From Language Models to Agents Chevron down icon Chevron up icon
The Rise of Generative AI: From Language Models to Agents Free Benefits with Your Book The modern LLM landscape From models to agentic applications Introducing LangChain Summary Questions
First Steps with LangChain Chevron down icon Chevron up icon
First Steps with LangChain Setting up dependencies for this book Exploring LangChain’s building blocks Running local models Multimodal AI applications Summary Review questions Subscribe for a free eBook
Building Workflows with LangGraph Chevron down icon Chevron up icon
Building Workflows with LangGraph LangGraph fundamentals Prompt engineering Working with short context windows Understanding memory mechanisms Summary Questions
Building Intelligent RAG Systems Chevron down icon Chevron up icon
Building Intelligent RAG Systems From indexes to intelligent retrieval Components of a RAG system From embeddings to search Breaking down the RAG pipeline Developing a corporate documentation chatbot Troubleshooting RAG systems Summary Questions Subscribe for a free eBook
Building Intelligent Agents Chevron down icon Chevron up icon
Building Intelligent Agents What is a tool? Defining tools Advanced tool-calling capabilities Incorporating tools into workflows What are agents? Summary Questions
Advanced Applications and Multi-Agent Systems Chevron down icon Chevron up icon
Advanced Applications and Multi-Agent Systems Agentic architectures Multi-agent architectures Building adaptive systems Exploring reasoning paths Agent memory Summary Questions Subscribe for a free eBook
Software Development and Data Analysis Agents Chevron down icon Chevron up icon
Software Development and Data Analysis Agents LLMs in software development Writing code with LLMs Applying LLM agents for data science Summary Questions
Evaluation and Testing Chevron down icon Chevron up icon
Evaluation and Testing Why evaluation matters What we evaluate: core agent capabilities How we evaluate: methodologies and approaches Evaluating LLM agents in practice Offline evaluation Summary Questions Subscribe for a free eBook
Production-Ready LLM Deployment and Observability Chevron down icon Chevron up icon
Production-Ready LLM Deployment and Observability Security considerations for LLM applications Deploying LLM apps How to observe LLM apps Cost management for LangChain applications Summary Questions
The Future of Generative Models: Beyond Scaling Chevron down icon Chevron up icon
The Future of Generative Models: Beyond Scaling The current state of generative AI The limitations of scaling and emerging alternatives Economic and industry transformation Societal implications Summary Subscribe for a free eBook
Other Books You May Enjoy Chevron down icon Chevron up icon
Other Books You May Enjoy
Index Chevron down icon Chevron up icon
Index Stay connected with Packt’s Generative AI community
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Mathematics of Machine Learning
Mathematics of Machine Learning
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Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
By Tivadar Danka
May 2025 | 730 pages
Icon Master linear algebra, calculus, and probability theory for ML
Icon Bridge the gap between theory and real-world applications
Icon Learn Python implementations of core mathematical concepts
Introduction Chevron down icon Chevron up icon
Introduction What is this book about? How to read this book Conventions used What this book covers To get the most out of this book
Part 1: Linear Algebra Chevron down icon Chevron up icon
Part 1: Linear Algebra
1 Vectors and Vector Spaces Chevron down icon Chevron up icon
1 Vectors and Vector Spaces 1.1 What is a vector space? 1.2 The basis 1.3 Vectors in practice 1.4 Summary 1.5 Problems
2 The Geometric Structure of Vector Spaces Chevron down icon Chevron up icon
2 The Geometric Structure of Vector Spaces 2.1 Norms and distances 2.2 Inner products, angles, and lots of reasons to care about them 2.3 Summary 2.4 Problems
3 Linear Algebra in Practice Chevron down icon Chevron up icon
3 Linear Algebra in Practice 3.1 Vectors in NumPy 3.2 Matrices, the workhorses of linear algebra 3.3 Summary 3.4 Problems
4 Linear Transformations Chevron down icon Chevron up icon
4 Linear Transformations 4.1 What is a linear transformation? 4.2 Change of basis 4.3 Linear transformations in the Euclidean plane 4.4 Determinants, or how linear transformations affect volume 4.5 Summary 4.6 Problems
5 Matrices and Equations Chevron down icon Chevron up icon
5 Matrices and Equations 5.1 Linear equations 5.2 The LU decomposition 5.3 Determinants in practice 5.4 Summary 5.5 Problems
6 Eigenvalues and Eigenvectors Chevron down icon Chevron up icon
6 Eigenvalues and Eigenvectors 6.1 Eigenvalues of matrices 6.2 Finding eigenvalue-eigenvector pairs 6.3 Eigenvectors, eigenspaces, and their bases 6.4 Summary 6.5 Problems
7 Matrix Factorizations Chevron down icon Chevron up icon
7 Matrix Factorizations 7.1 Special transformations 7.2 Self-adjoint transformations and the spectral decomposition theorem 7.3 The singular value decomposition 7.4 Orthogonal projections 7.5 Computing eigenvalues 7.6 The QR algorithm 7.7 Summary 7.8 Problems
8 Matrices and Graphs Chevron down icon Chevron up icon
8 Matrices and Graphs 8.1 The directed graph of a nonnegative matrix 8.2 Benefits of the graph representation 8.3 The Frobenius normal form 8.4 Summary 8.5 Problems
References Chevron down icon Chevron up icon
References
Part 2: Calculus Chevron down icon Chevron up icon
Part 2: Calculus
9 Functions Chevron down icon Chevron up icon
9 Functions 9.1 Functions in theory 9.2 Functions in practice 9.3 Summary 9.4 Problems
10 Numbers, Sequences, and Series Chevron down icon Chevron up icon
10 Numbers, Sequences, and Series 10.1 Numbers 10.2 Sequences 10.3 Series 10.4 Summary 10.5 Problems
11 Topology, Limits, and Continuity Chevron down icon Chevron up icon
11 Topology, Limits, and Continuity 11.1 Topology 11.2 Limits 11.3 Continuity 11.4 Summary 11.5 Problems
12 Differentiation Chevron down icon Chevron up icon
12 Differentiation 12.1 Differentiation in theory 12.2 Differentiation in practice 12.3 Summary 12.4 Problems
13 Optimization Chevron down icon Chevron up icon
13 Optimization 13.1 Minima, maxima, and derivatives 13.2 The basics of gradient descent 13.3 Why does gradient descent work? 13.4 Summary 13.5 Problems
14 Integration Chevron down icon Chevron up icon
14 Integration 14.1 Integration in theory 14.2 Integration in practice 14.3 Summary 14.4 Problems Join our community on Discord
References Chevron down icon Chevron up icon
References
Part 3: Multivariable Calculus Chevron down icon Chevron up icon
Part 3: Multivariable Calculus
15 Multivariable Functions Chevron down icon Chevron up icon
15 Multivariable Functions 15.1 What is a multivariable function? 15.2 Linear functions in multiple variables 15.3 The curse of dimensionality 15.4 Summary
16 Derivatives and Gradients Chevron down icon Chevron up icon
16 Derivatives and Gradients 16.1 Partial and total derivatives 16.2 Derivatives of vector-valued functions 16.3 Summary 16.4 Problems
17 Optimization in Multiple Variables Chevron down icon Chevron up icon
17 Optimization in Multiple Variables 17.1 Multivariable functions in code 17.2 Minima and maxima, revisited 17.3 Gradient descent in its full form 17.4 Summary 17.5 Problems
References Chevron down icon Chevron up icon
References
Part 4: Probability Theory Chevron down icon Chevron up icon
Part 4: Probability Theory
18 What is Probability? Chevron down icon Chevron up icon
18 What is Probability? 18.1 The language of thinking 18.2 The axioms of probability 18.3 Conditional probability 18.4 Summary 18.5 Problems
19 Random Variables and Distributions Chevron down icon Chevron up icon
19 Random Variables and Distributions 19.1 Random variables 19.2 Discrete distributions 19.3 Real-valued distributions 19.4 Density functions 19.5 Summary 19.6 Problems
20 The Expected Value Chevron down icon Chevron up icon
20 The Expected Value 20.1 Discrete random variables 20.2 Continuous random variables 20.3 Properties of the expected value 20.4 Variance 20.5 The law of large numbers 20.6 Information theory 20.7 The Maximum Likelihood Estimation 20.8 Summary 20.9 Problems
References Chevron down icon Chevron up icon
References
Part 5: Appendix Chevron down icon Chevron up icon
Part 5: Appendix
Other Books You May Enjoy Chevron down icon Chevron up icon
Other Books You May Enjoy
Index Chevron down icon Chevron up icon
Index
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Machine Learning for Algorithmic Trading
Machine Learning for Algorithmic Trading
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Full star icon Full star icon Full star icon Full star icon Empty star icon 4
By Stefan Jansen
July 2020 | 820 pages
Icon Design, train, and evaluate machine learning algorithms that underpin automated trading strategies
Icon Create a research and strategy development process to apply predictive modeling to trading decisions
Icon Leverage NLP and deep learning to extract tradeable signals from market and alternative data
Machine Learning for Trading – From Idea to Execution Chevron down icon Chevron up icon
Machine Learning for Trading – From Idea to Execution The rise of ML in the investment industry Designing and executing an ML-driven strategy ML for trading – strategies and use cases Summary
Market and Fundamental Data – Sources and Techniques Chevron down icon Chevron up icon
Market and Fundamental Data – Sources and Techniques Market data reflects its environment Working with high-frequency data API access to market data How to work with fundamental data Efficient data storage with pandas Summary
Alternative Data for Finance – Categories and Use Cases Chevron down icon Chevron up icon
Alternative Data for Finance – Categories and Use Cases The alternative data revolution Sources of alternative data Criteria for evaluating alternative data The market for alternative data Working with alternative data Summary
Financial Feature Engineering – How to Research Alpha Factors Chevron down icon Chevron up icon
Financial Feature Engineering – How to Research Alpha Factors Alpha factors in practice – from data to signals Building on decades of factor research Engineering alpha factors that predict returns From signals to trades – Zipline for backtests Separating signal from noise with Alphalens Alpha factor resources Summary
Portfolio Optimization and Performance Evaluation Chevron down icon Chevron up icon
Portfolio Optimization and Performance Evaluation How to measure portfolio performance How to manage portfolio risk and return Trading and managing portfolios with Zipline Measuring backtest performance with pyfolio Summary
The Machine Learning Process Chevron down icon Chevron up icon
The Machine Learning Process How machine learning from data works The machine learning workflow Summary
Linear Models – From Risk Factors to Return Forecasts Chevron down icon Chevron up icon
Linear Models – From Risk Factors to Return Forecasts From inference to prediction The baseline model – multiple linear regression How to run linear regression in practice How to build a linear factor model Regularizing linear regression using shrinkage How to predict returns with linear regression Linear classification Summary
The ML4T Workflow – From Model to Strategy Backtesting Chevron down icon Chevron up icon
The ML4T Workflow – From Model to Strategy Backtesting How to backtest an ML-driven strategy Backtesting pitfalls and how to avoid them How a backtesting engine works backtrader – a flexible tool for local backtests Zipline – scalable backtesting by Quantopian Summary
Time-Series Models for Volatility Forecasts and Statistical Arbitrage Chevron down icon Chevron up icon
Time-Series Models for Volatility Forecasts and Statistical Arbitrage Tools for diagnostics and feature extraction How to diagnose and achieve stationarity Univariate time-series models Multivariate time-series models Cointegration – time series with a shared trend Statistical arbitrage with cointegration Summary
Bayesian ML – Dynamic Sharpe Ratios and Pairs Trading Chevron down icon Chevron up icon
Bayesian ML – Dynamic Sharpe Ratios and Pairs Trading How Bayesian machine learning works Probabilistic programming with PyMC3 Bayesian ML for trading Summary
Random Forests – A Long-Short Strategy for Japanese Stocks Chevron down icon Chevron up icon
Random Forests – A Long-Short Strategy for Japanese Stocks Decision trees – learning rules from data Random forests – making trees more reliable Long-short signals for Japanese stocks Summary
Boosting Your Trading Strategy Chevron down icon Chevron up icon
Boosting Your Trading Strategy Getting started – adaptive boosting Gradient boosting – ensembles for most tasks Using XGBoost, LightGBM, and CatBoost A long-short trading strategy with boosting Boosting for an intraday strategy Summary
Data-Driven Risk Factors and Asset Allocation with Unsupervised Learning Chevron down icon Chevron up icon
Data-Driven Risk Factors and Asset Allocation with Unsupervised Learning Dimensionality reduction PCA for trading Clustering Hierarchical clustering for optimal portfolios Summary
Text Data for Trading – Sentiment Analysis Chevron down icon Chevron up icon
Text Data for Trading – Sentiment Analysis ML with text data – from language to features From text to tokens – the NLP pipeline Counting tokens – the document-term matrix NLP for trading Summary
Topic Modeling – Summarizing Financial News Chevron down icon Chevron up icon
Topic Modeling – Summarizing Financial News Learning latent topics – Goals and approaches Probabilistic latent semantic analysis Latent Dirichlet allocation Modeling topics discussed in earnings calls Topic modeling for with financial news Summary
Word Embeddings for Earnings Calls and SEC Filings Chevron down icon Chevron up icon
Word Embeddings for Earnings Calls and SEC Filings How word embeddings encode semantics How to use pretrained word vectors Custom embeddings for financial news word2vec for trading with SEC filings Sentiment analysis using doc2vec embeddings New frontiers – pretrained transformer models Summary
Deep Learning for Trading Chevron down icon Chevron up icon
Deep Learning for Trading Deep learning – what's new and why it matters Designing an NN A neural network from scratch in Python Popular deep learning libraries Optimizing an NN for a long-short strategy Summary
CNNs for Financial Time Series and Satellite Images Chevron down icon Chevron up icon
CNNs for Financial Time Series and Satellite Images How CNNs learn to model grid-like data CNNs for satellite images and object detection CNNs for time-series data – predicting returns Summary
RNNs for Multivariate Time Series and Sentiment Analysis Chevron down icon Chevron up icon
RNNs for Multivariate Time Series and Sentiment Analysis How recurrent neural nets work RNNs for time series with TensorFlow 2 RNNs for text data Summary
Autoencoders for Conditional Risk Factors and Asset Pricing Chevron down icon Chevron up icon
Autoencoders for Conditional Risk Factors and Asset Pricing Autoencoders for nonlinear feature extraction Implementing autoencoders with TensorFlow 2 A conditional autoencoder for trading Summary
Generative Adversarial Networks for Synthetic Time-Series Data Chevron down icon Chevron up icon
Generative Adversarial Networks for Synthetic Time-Series Data Creating synthetic data with GANs How to build a GAN using TensorFlow 2 TimeGAN for synthetic financial data Summary
Deep Reinforcement Learning – Building a Trading Agent Chevron down icon Chevron up icon
Deep Reinforcement Learning – Building a Trading Agent Elements of a reinforcement learning system How to solve reinforcement learning problems Solving dynamic programming problems Q-learning – finding an optimal policy on the go Deep RL for trading with the OpenAI Gym Summary
Conclusions and Next Steps Chevron down icon Chevron up icon
Conclusions and Next Steps Key takeaways and lessons learned ML for trading in practice Conclusion
References Chevron down icon Chevron up icon
References
Index Chevron down icon Chevron up icon
Index
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Python Machine Learning By Example
Python Machine Learning By Example
Read now
Full star icon Full star icon Full star icon Full star icon Half star icon 4.3
By Yuxi (Hayden) Liu
July 2024 | 526 pages
Icon Discover new and updated content on NLP transformers, PyTorch, and computer vision modeling
Icon Includes a dedicated chapter on best practices and additional best practice tips throughout the book to improve your ML solutions
Icon Implement ML models, such as neural networks and linear and logistic regression, from scratch
Getting Started with Machine Learning and Python Chevron down icon Chevron up icon
Getting Started with Machine Learning and Python An introduction to machine learning Knowing the prerequisites Getting started with three types of machine learning Digging into the core of machine learning Data preprocessing and feature engineering Combining models Installing software and setting up Summary Exercises Join our book’s Discord space
Building a Movie Recommendation Engine with Naïve Bayes Chevron down icon Chevron up icon
Building a Movie Recommendation Engine with Naïve Bayes Getting started with classification Exploring Naïve Bayes Implementing Naïve Bayes Building a movie recommender with Naïve Bayes Evaluating classification performance Tuning models with cross-validation Summary Exercises References
Predicting Online Ad Click-Through with Tree-Based Algorithms Chevron down icon Chevron up icon
Predicting Online Ad Click-Through with Tree-Based Algorithms A brief overview of ad click-through prediction Getting started with two types of data – numerical and categorical Exploring a decision tree from the root to the leaves Implementing a decision tree from scratch Implementing a decision tree with scikit-learn Predicting ad click-through with a decision tree Ensembling decision trees – random forests Ensembling decision trees – gradient-boosted trees Summary Exercises Join our book’s Discord space
Predicting Online Ad Click-Through with Logistic Regression Chevron down icon Chevron up icon
Predicting Online Ad Click-Through with Logistic Regression Converting categorical features to numerical – one-hot encoding and ordinal encoding Classifying data with logistic regression Training a logistic regression model Training on large datasets with online learning Handling multiclass classification Implementing logistic regression using TensorFlow Summary Exercises
Predicting Stock Prices with Regression Algorithms Chevron down icon Chevron up icon
Predicting Stock Prices with Regression Algorithms What is regression? Mining stock price data Getting started with feature engineering Estimating with linear regression Estimating with decision tree regression Implementing a regression forest Evaluating regression performance Predicting stock prices with the three regression algorithms Summary Exercises Join our book’s Discord space
Predicting Stock Prices with Artificial Neural Networks Chevron down icon Chevron up icon
Predicting Stock Prices with Artificial Neural Networks Demystifying neural networks Building neural networks Picking the right activation functions Preventing overfitting in neural networks Predicting stock prices with neural networks Summary Exercises
Mining the 20 Newsgroups Dataset with Text Analysis Techniques Chevron down icon Chevron up icon
Mining the 20 Newsgroups Dataset with Text Analysis Techniques How computers understand language – NLP Touring popular NLP libraries and picking up NLP basics Getting the newsgroups data Exploring the newsgroups data Thinking about features for text data Visualizing the newsgroups data with t-SNE Summary Exercises Join our book’s Discord space
Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling Chevron down icon Chevron up icon
Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling Learning without guidance – unsupervised learning Getting started with k-means clustering Clustering newsgroups dataset Discovering underlying topics in newsgroups Summary Exercises
Recognizing Faces with Support Vector Machine Chevron down icon Chevron up icon
Recognizing Faces with Support Vector Machine Finding the separating boundary with SVM Classifying face images with SVM Estimating with support vector regression Summary Exercises Join our book’s Discord space
Machine Learning Best Practices Chevron down icon Chevron up icon
Machine Learning Best Practices Machine learning solution workflow Best practices in the data preparation stage Best practices in the training set generation stage Best practices in the model training, evaluation, and selection stage Best practices in the deployment and monitoring stage Summary Exercises
Categorizing Images of Clothing with Convolutional Neural Networks Chevron down icon Chevron up icon
Categorizing Images of Clothing with Convolutional Neural Networks Getting started with CNN building blocks Architecting a CNN for classification Exploring the clothing image dataset Classifying clothing images with CNNs Boosting the CNN classifier with data augmentation Improving the clothing image classifier with data augmentation Advancing the CNN classifier with transfer learning Summary Exercises Join our book’s Discord space
Making Predictions with Sequences Using Recurrent Neural Networks Chevron down icon Chevron up icon
Making Predictions with Sequences Using Recurrent Neural Networks Introducing sequential learning Learning the RNN architecture by example Training an RNN model Overcoming long-term dependencies with LSTM Analyzing movie review sentiment with RNNs Revisiting stock price forecasting with LSTM Writing your own War and Peace with RNNs Summary Exercises
Advancing Language Understanding and Generation with the Transformer Models Chevron down icon Chevron up icon
Advancing Language Understanding and Generation with the Transformer Models Understanding self-attention Exploring the Transformer’s architecture Improving sentiment analysis with BERT and Transformers Generating text using GPT Summary Exercises Join our book’s Discord space
Building an Image Search Engine Using CLIP: a Multimodal Approach Chevron down icon Chevron up icon
Building an Image Search Engine Using CLIP: a Multimodal Approach Introducing the CLIP model Getting started with the dataset Finding images with words Summary Exercises References
Making Decisions in Complex Environments with Reinforcement Learning Chevron down icon Chevron up icon
Making Decisions in Complex Environments with Reinforcement Learning Setting up the working environment Introducing OpenAI Gym and Gymnasium Introducing reinforcement learning with examples Solving the FrozenLake environment with dynamic programming Performing Monte Carlo learning Solving the Blackjack problem with the Q-learning algorithm Summary Exercises Join our book’s Discord space
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Workflow Automation with Microsoft Power Automate
Workflow Automation with Microsoft Power Automate
Read now
Full star icon Full star icon Full star icon Full star icon Full star icon 5
By Aaron Guilmette
December 2025 | 544 pages
Icon Build flows faster using Copilot, AI Builder, and Gen AI tools
Icon Learn to create robust cloud and desktop automations through real-world examples
Icon Implement best practices for security, governance, and monitoring
Part 1: The Basics Chevron down icon Chevron up icon
Part 1: The Basics
Introducing Microsoft Power Automate Chevron down icon Chevron up icon
Introducing Microsoft Power Automate Free Benefits with Your Book What is Power Automate? Choosing what to automate Getting up to speed on general terminology Expanding into Power Automate terminology Getting Power Automate Summary
Getting Started with Power Automate Chevron down icon Chevron up icon
Getting Started with Power Automate Logging in to Power Automate Learning how a flow works Creating your first flow Summary
Working with Email Chevron down icon Chevron up icon
Working with Email Learning about email connectors and actions Working with email Summary
Copying Files Chevron down icon Chevron up icon
Copying Files Learning about file connectors and actions Working with files Summary
Creating Button Flows Chevron down icon Chevron up icon
Creating Button Flows Learning about button flows Creating a button flow to email a manager Executing a button flow Verifying the flow Summary
Generating Push Notifications Chevron down icon Chevron up icon
Generating Push Notifications Learning about push notifications Configuring a notification for emails from your manager Introducing conditions Creating the flow Reviewing a notification Reworking the flow Summary
Working with Shared Flows Chevron down icon Chevron up icon
Working with Shared Flows Understanding shared flows Sharing a cloud flow Sharing a desktop flow Sharing a flow with run-only permissions Managing shared flows Summary
Part 2: Advanced Operations and Flows Chevron down icon Chevron up icon
Part 2: Advanced Operations and Flows
Working with Conditions Chevron down icon Chevron up icon
Working with Conditions Understanding condition operators Using dynamic values and multiple conditions Summary
Understanding Expressions and Functions Chevron down icon Chevron up icon
Understanding Expressions and Functions Learning about expressions and functions Working with strings and text Manipulating times and dates Working with arrays Exploring further Summary
Getting Started with Approvals Chevron down icon Chevron up icon
Getting Started with Approvals Understanding Dataverse Creating an approval flow Working with the approval flow Summary
Working with Multiple Approvals Chevron down icon Chevron up icon
Working with Multiple Approvals Working with sequential approvals Working with parallel approvals Working with advanced scenarios Creating a basic sequential approval Adding parallel branches Summary
Posting Approvals to Teams Chevron down icon Chevron up icon
Posting Approvals to Teams Understanding the flow Configuring prerequisites Configuring an approval flow to use Teams Getting the requester’s information Testing the flow Summary
Using Databases Chevron down icon Chevron up icon
Using Databases Understanding database connectors, triggers, and actions Connecting to a database Creating a connection to a database Adding content to a database Summary
Working with Microsoft Forms Chevron down icon Chevron up icon
Working with Microsoft Forms Understanding the Forms connector triggers and actions Creating a basic form Processing a form with Power Automate Testing the flow Verifying the result Summary
Posting to Slack Chevron down icon Chevron up icon
Posting to Slack Understanding the Planner and Slack connectors Setting up the prerequisites Using Planner to trigger a flow Posting an update to a Slack channel Testing the flow Further exploration Summary
Accepting User Input Chevron down icon Chevron up icon
Accepting User Input Understanding the user input options Creating the flow Executing the flow Verifying the flow execution Expanding further Summary
Automating Entra ID Chevron down icon Chevron up icon
Automating Entra ID Technical requirements Learning about connectors and actions Configuring prerequisites Creating an HTTP flow Testing the flow Verifying the flow Expanding further Summary
Part 3: Robotic Process Automation, AI Models, Copilot, and Beyond Chevron down icon Chevron up icon
Part 3: Robotic Process Automation, AI Models, Copilot, and Beyond
Introducing Robotic Process Automation Chevron down icon Chevron up icon
Introducing Robotic Process Automation Technical requirements Learning about actions and passing variables Configuring prerequisites Configuring the browser extensions Summary
Contributing to an Access Database with RPA Chevron down icon Chevron up icon
Contributing to an Access Database with RPA Technical requirements Configuring prerequisites Configuring the flow Testing the flow Verifying the flow Summary
Automating Web Pages with RPA Chevron down icon Chevron up icon
Automating Web Pages with RPA Navigating web pages Scraping data Entering data Taking screenshots Summary
Introducing AI Models and Generative AI Chevron down icon Chevron up icon
Introducing AI Models and Generative AI Learning about AI models Learning about generative AI models Exploring Microsoft Copilot for Power Platform Technical requirements Summary
Creating a Sentiment Analysis Flow Chevron down icon Chevron up icon
Creating a Sentiment Analysis Flow What is sentiment analysis, anyway? Licensing prerequisites Configuring solution prerequisites Configuring a sentiment analysis flow Testing the flow Further exploration Summary
Using Copilot to Create and Manage Flows Chevron down icon Chevron up icon
Using Copilot to Create and Manage Flows Creating a flow with Copilot Editing a flow Describing a flow Summary
Using Generative AI to Summarize Documents Chevron down icon Chevron up icon
Using Generative AI to Summarize Documents Licensing prerequisites Configuring solution prerequisites Creating the flow Testing the flow Further exploration Summary
Part 4: Administration Chevron down icon Chevron up icon
Part 4: Administration
Exporting, Importing, and Distributing Flows Chevron down icon Chevron up icon
Exporting, Importing, and Distributing Flows Exporting a flow Importing a flow Distributing a flow Summary
Monitoring and Troubleshooting Flows Chevron down icon Chevron up icon
Monitoring and Troubleshooting Flows Monitoring flows Viewing a run history Resubmitting a flow Reviewing email error reports Resolving authentication errors Examining detailed errors with the flow checker Summary
Governance in the Power Platform Chevron down icon Chevron up icon
Governance in the Power Platform Understanding governance concepts Working with the Power Platform Center of Excellence Identifying compliance controls Summary
Unlock Your Exclusive Benefits Chevron down icon Chevron up icon
Unlock Your Exclusive Benefits Unlock this Book’s Free Benefits in 3 Easy Steps
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Index
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Learn Model Context Protocol with Python
Learn Model Context Protocol with Python
Read now
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5
By Christoffer Noring
October 2025 | 304 pages
Icon The only resource you'll need to build, test, and deploy MCP servers and clients
Icon Take a modern approach toward building, testing, and securing distributed agentic AI apps
Icon Get clear, professional guidance on developing for both LLM and non-LLM clients
Icon Purchase of the print or Kindle book includes a free PDF eBook
Introduction to the Model Context Protocol Chevron down icon Chevron up icon
Introduction to the Model Context Protocol Getting the most out of this book – get to know your free benefits How we got here, from SOAP to REST to GraphQL to gRPC to MCP The need for a standard Endless possibilities: know how to prompt, and a world of MCP servers is your oyster What is the MCP? Summary and next steps: now what?
Explaining the Model Context Protocol Chevron down icon Chevron up icon
Explaining the Model Context Protocol Learning about the protocol by implementing it Transports in MCP Summary Assignment Solution Quiz
Building and Testing Servers Chevron down icon Chevron up icon
Building and Testing Servers A STDIO server Concepts Runtimes Testing the server First server Summary Assignment – an e-commerce STDIO server Solution Quiz References
Building SSE Servers Chevron down icon Chevron up icon
Building SSE Servers SSE concepts Creating an SSE server as a web app Testing with SSE Creating an SSE server Summary Assignment – SSE server Solution Quiz References
Streamable HTTP Chevron down icon Chevron up icon
Streamable HTTP Streamable HTTP versus SSE, and why it is the new standard Streamable HTTP in MCP Resumability Notifications Creating and testing a server with Streamable HTTP Testing the server Testing out resumability Summary Assignment Solution Quiz References
Advanced Servers Chevron down icon Chevron up icon
Advanced Servers Why this low-level approach? Context managers Context managers in MCP servers Low-level access Organizing your architecture Summary Assignment Solution Quiz
Building Clients Chevron down icon Chevron up icon
Building Clients Building a client Exercise: Building a client Clients with LLMs Working with an LLM Exercise: Integrating the LLM Summary Assignment Solution Quiz
Consuming Servers Chevron down icon Chevron up icon
Consuming Servers Consuming with hosts such as Claude Desktop and VS Code Installation process Adding a server Local and global install Tips and tricks Security aspects Suggestion for servers Summary Assignment Quiz
Sampling Chevron down icon Chevron up icon
Sampling Why sampling? Implementing sampling Summary Assignment Solution Quiz
Elicitation Chevron down icon Chevron up icon
Elicitation Why elicitation? Implementing elicitation Elicitation flow JSON-RPC messages Implementing the server-side functionality Testing elicitation with VS Code Summary Assignment Solution Quiz
Securing Your Application Chevron down icon Chevron up icon
Securing Your Application Basic authentication Hardening security with JWT OAuth2 Summary Assignment 1: Secure your MCP server with basic authentication Solution 1 Assignment 2: Secure your MCP server with JWT Solution 2 Quiz Resources
Bringing MCP Apps to Production Chevron down icon Chevron up icon
Bringing MCP Apps to Production Architecture and design Packaging and distribution Testing and deployment automation Operations and observability Summary Resources
Unlock Your Book’s Exclusive Benefits Chevron down icon Chevron up icon
Unlock Your Book’s Exclusive Benefits How to unlock these benefits in three easy steps
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Understanding the LLM Twin Concept and Architecture Icon Tooling and Installation Icon Data Engineering Icon RAG Feature Pipeline Icon Supervised Fine-Tuning Icon Fine-Tuning with Preference Alignment Icon Evaluating LLMs Icon Inference Optimization Icon RAG Inference Pipeline Icon Inference Pipeline Deployment Icon MLOps and LLMOps Icon MLOps Principles Icon Other Books You May Enjoy Icon Index Icon
C# 14 and .NET 10 – Modern Cross-Platform Development Fundamentals C# 14 and .NET 10 – Modern Cross-Platform Development Fundamentals
Nov-25
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Hello, C#! Welcome, .NET! Icon Speaking C# Icon Controlling Flow, Converting Types, and Handling Exceptions Icon Writing, Debugging, and Testing Functions Icon Building Your Own Types with Object-Oriented Programming Icon Implementing Interfaces and Inheriting Classes Icon Packaging and Distributing .NET Types Icon Working with Common .NET Types Icon Working with Files, Streams, and Serialization Icon Working with Data Using Entity Framework Core Icon Querying and Manipulating Data Using LINQ Icon Introducing Modern Web Development Using .NET Icon Building Websites Using ASP.NET Core Icon Building Interactive Web Components Using Blazor Icon Building and Consuming Web Services Icon Unlock Your Exclusive Benefits Icon Epilogue Icon Index Icon
Practical C# Projects with .NET Practical C# Projects with .NET
Apr-26
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Part 1: Building Console Applications Icon Chapter 1: Building an Adventure Game Console App with .NET and Spectre.Console Icon Chapter 2: Recreating the Enigma Machine with Object-Oriented Programming Icon Chapter 3: Exploring Infinite Worlds with Procedural Generation Icon Chapter 4: Building a Data-Driven Role-Playing Game Icon Part 2: Web, Mobile, and Desktop Applications Icon Chapter 5: Tracking Collectible Card Games with ASP.NET Minimal APIs and Entity Framework Icon Chapter 6: Creating a Trading Card Tracker Using Blazor WebAssembly Icon Chapter 7: Building a Cross-Platform Chatbot with Uno Platform and ELIZA Icon Part 3: Building AI-powered side projects Icon Chapter 8: Building Conversational AI Partners with Ollama and Microsoft.Extensions.AI Icon Chapter 9: Building an AI Librarian with Microsoft Agent Framework Icon Chapter 10: Building and Monitoring a Model Context Protocol Server with Aspire Icon Chapter 11: Predicting Values with Machine Learning, IoT Data, and ML.NET Icon Part 4: Cross-platform Game Development with MonoGame Icon Chapter 12: Recreating Pong in MonoGame with C# Icon Chapter 13: Building Larger Games with Graphics, Collisions, and AI Icon Chapter 14: Succeeding with Side Projects Icon Chapter 15: Unlock Your Exclusive Benefits Icon Index Icon
Building Natural Language and LLM Pipelines Building Natural Language and LLM Pipelines
Dec-25
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Part 1: The Foundation of Reliable AI Icon Chapter 1: Introduction to Natural Language Processing Pipelines Icon Chapter 2: Diving Deep into Large Language Models Icon Part 2: Building The Tool Layer with Haystack Icon Chapter 3: Introduction to Haystack by deepset Icon Chapter 4: Bringing Components Together – Haystack Pipelines for Different Use Cases Icon Chapter 5: Haystack Pipeline Development with Custom Components Icon Chapter 6: Building Reproducible and Production-Ready RAG Systems Icon Part 3: Deployment and Agentic Orchestration Icon Chapter 7: Deploying Haystack-Based Applications Icon Chapter 8: Hands-On Projects Icon Part 4: The Future of Agentic AI Icon Chapter 9: Future Trends and Beyond Icon Chapter 10: Epilogue: The Architecture of Agentic AI Icon Chapter 11: Unlock Your Exclusive Benefits Icon Index Icon
Building Agents with OpenAI Agents SDK Building Agents with OpenAI Agents SDK
Oct-25
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Part 1: AI Agents Icon Introduction to AI Agents Icon Introduction to OpenAI Agents SDK Icon Environment Setup and Developing Your First Agent Icon Part 2: OpenAI Agents SDK Icon Agent Tools and MCPs Icon Memory and Knowledge Icon Multi-Agent Systems and Handoffs Icon Model and Context Management Icon Agent System Management Icon Part 3: Build AI Agents Icon Building AI Agents and Agentic Systems Icon Other Books You May Enjoy Icon Index Icon
The GitHub Copilot Handbook The GitHub Copilot Handbook
Nov-25
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What is GitHub Copilot? Icon GitHub Copilot Explained Icon Getting Started with Generative AI Icon Choosing the Right GitHub Copilot Plan Icon Getting Started with GitHub Copilot Icon Mastering GitHub Copilot in Your IDE: Inline Suggestions, Chat, and Agent Mode Icon Going Beyond Code: Debugging, Terminal, and Collaboration with GitHub Copilot Icon Exploring GitHub Copilot Integrations Icon Collaborating with Copilot on GitHub.com: Issues, PRs, Reviews, and Coding Agent Icon Extending GitHub Copilot with the Model Context Protocol (MCP) Icon Getting the Most Out of GitHub Copilot Icon Navigating the GitHub Copilot Learning Curve Icon Building an Internal GitHub Copilot Community Icon Changing the Narrative: Reframing Engineering with AI Icon Unlock Your Exclusive Benefits Icon Other Books You May Enjoy Icon Index Icon
scikit-learn Cookbook scikit-learn Cookbook
Dec-25
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Chapter 1: Common Conventions and API Elements of scikit-learn Icon Chapter 2: Pre-Model Workflow and Data Preprocessing Icon Chapter 3: Dimensionality Reduction Techniques Icon Chapter 4: Building Models with Distance Metrics and Nearest Neighbors Icon Chapter 5: Linear Models and Regularization Icon Chapter 6: Advanced Logistic Regression and Extensions Icon Chapter 7: Support Vector Machines and Kernel Methods Icon Chapter 8: Tree-Based Algorithms and Ensemble Methods Icon Chapter 9: Text Processing and Multiclass Classification Icon Chapter 10: Clustering Techniques Icon Chapter 11: Novelty and Outlier Detection Icon Chapter 12: Cross-Validation and Model Evaluation Techniques Icon Chapter 13: Deploying scikit-learn Models in Production Icon Chapter 14: Unlock Your Exclusive Benefits Icon Index Icon Other Books You May Enjoy Icon
AI Agents in Practice AI Agents in Practice
Aug-25
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Part 1: Foundations of AI Workflows and the Rise of AI Agents Icon Evolution of GenAI Workflows Icon The Rise of AI Agents Icon Part 2: Designing, Building, and Scaling AI Agents Icon The Need for an AI Orchestrator Icon The Need for Memory and Context Management Icon The Need for Tools and External Integrations Icon Building Your First AI Agent with LangChain Icon Multi-Agent Applications Icon Part 3: Road to an Open, Agentic Ecosystem Icon Orchestrating Intelligence: Blueprint for Next-Gen Agent Protocols Icon Navigating Ethical Challenges in Real-World AI Icon Other Books You May Enjoy Icon Index Icon

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