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Mathematics of Machine Learning
Mathematics of Machine Learning
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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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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 Full star icon 5
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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Learn Model Context Protocol with Python
Learn Model Context Protocol with Python
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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
Other Books You May Enjoy Chevron down icon Chevron up icon
Other Books You May Enjoy
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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 Half star icon Empty star icon 3.8
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?
Other Books You May Enjoy Chevron down icon Chevron up icon
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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 4.9
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
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Index
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Coding Roblox Games Made Easy, Second Edition
Coding Roblox Games Made Easy, Second Edition
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Full star icon Full star icon Full star icon Full star icon Half star icon 4.5
By Zander Brumbaugh
June 2022 | 302 pages
Icon Begin coding in Luau: build player avatars, battlefields, game physics, countdown timers and more
Icon Learn tips, tricks, best practices, and advanced Roblox coding techniques to create 3D games
Icon Join the book club to discuss queries, provide solutions, and ask Zander for advice on your games
Section I: Introduction to Roblox Development Chevron down icon Chevron up icon
Section I: Introduction to Roblox Development
Introducing Roblox Development Chevron down icon Chevron up icon
Introducing Roblox Development Technical requirements Learning the benefits of Roblox development Discovering developer types Gaining a perspective about your early projects Summary Worksheet
Knowing Your Work Environment Chevron down icon Chevron up icon
Knowing Your Work Environment Technical requirements What is an experience? Traversing the Create page Getting started with Roblox Studio Taking advantage of Roblox’s resources Summary Worksheet
Section II: Programming in Roblox Chevron down icon Chevron up icon
Section II: Programming in Roblox
Introduction to Luau Chevron down icon Chevron up icon
Introduction to Luau Technical requirements Learning about data types and creating variables Declaring and using loops Learning about functions and events Demonstrating programming style and efficiency Summary Worksheet
Roblox Programming Scenarios Chevron down icon Chevron up icon
Roblox Programming Scenarios Technical requirements Understanding the client-server model Using Roblox services Working with physics Adding peripheral experience aspects Summary Worksheet
Creating an Obby Chevron down icon Chevron up icon
Creating an Obby Technical requirements Setting up the backend Making obby stages Setting up the frontend Testing and publication Summary Worksheet
Creating a Battle Royale Game Chevron down icon Chevron up icon
Creating a Battle Royale Game Technical requirements Setting up the backend Managing player data Creating weapons Setting up the round system Local replication Spawning loot Setting up the frontend Summary Worksheet
Section III: The Logistics of Game Production Chevron down icon Chevron up icon
Section III: The Logistics of Game Production
The Three Ms Chevron down icon Chevron up icon
The Three Ms Technical requirements Mechanics Monetization Marketing Reviewing what you’ve learned Metaverse Summary Worksheet
50 Cool Things to Do on Roblox Chevron down icon Chevron up icon
50 Cool Things to Do on Roblox Technical requirements Programming challenges Experience systems Experience ideas Roblox features Other development types Summary
Worksheet Answers Chevron down icon Chevron up icon
Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7
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Index
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scikit-learn Cookbook
scikit-learn Cookbook
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By John Sukup
December 2025 | 388 pages
Icon Solve complex business problems with data-driven approaches
Icon Master tools associated with developing predictive and prescriptive models
Icon Build robust ML pipelines for real-world applications, avoiding common pitfalls
Icon Free with your book: PDF Copy, AI Assistant, and Next-Gen Reader
Chapter 1: Common Conventions and API Elements of scikit-learn Chevron down icon Chevron up icon
Chapter 1: Common Conventions and API Elements of scikit-learn Technical requirements Introduction to scikit-learn’s design philosophy Understanding estimators Transformers and the transform() method Handling custom estimators and transformers Pipelines and workflow automation Common attributes and methods Hyperparameter tuning with search methods Working with metadata: Tags and more Best practices for API usage
Chapter 2: Pre-Model Workflow and Data Preprocessing Chevron down icon Chevron up icon
Chapter 2: Pre-Model Workflow and Data Preprocessing Technical requirements The impact of raw data on model performance Handling missing data Scaling techniques Encoding categorical variables Introduction to pipelines in scikit-learn Feature engineering Practical exercises on data preprocessing
Chapter 3: Dimensionality Reduction Techniques Chevron down icon Chevron up icon
Chapter 3: Dimensionality Reduction Techniques Technical requirements Introduction to dimensionality reduction Transforming datasets with PCA Maximizing class separability with LDA t-SNE and data visualization Impact on model performance Practical exercises in dimensionality reduction
Chapter 4: Building Models with Distance Metrics and Nearest Neighbors Chevron down icon Chevron up icon
Chapter 4: Building Models with Distance Metrics and Nearest Neighbors Technical requirements Introduction to distance metrics Understanding KNNs Distance metrics overview Hyperparameter tuning in KNN Evaluating KNN performance Practical exercises with KNN models
Chapter 5: Linear Models and Regularization Chevron down icon Chevron up icon
Chapter 5: Linear Models and Regularization Technical requirements Introduction to linear models Ridge and Lasso regression ElasticNet and regularization Regularization theory and practice Regression and regularization Practical exercises with regularization techniques
Chapter 6: Advanced Logistic Regression and Extensions Chevron down icon Chevron up icon
Chapter 6: Advanced Logistic Regression and Extensions Technical requirements Overview of logistic regression Multiclass classification techniques Regularization in logistic regression Multilabel classification concepts Model evaluation metrics Practical exercises with advanced logistic regression
Chapter 7: Support Vector Machines and Kernel Methods Chevron down icon Chevron up icon
Chapter 7: Support Vector Machines and Kernel Methods Technical requirements Introduction to SVMs Kernel functions and their applications Tuning SVM parameters SVMs in high-dimensional spaces Evaluating SVM models Practical exercises with SVMs
Chapter 8: Tree-Based Algorithms and Ensemble Methods Chevron down icon Chevron up icon
Chapter 8: Tree-Based Algorithms and Ensemble Methods Technical requirements Introduction to decision trees Random forests and bagging Gradient boosting machines Hyperparameter tuning for trees and ensembles Comparing ensemble methods Practical exercises with tree-based models
Chapter 9: Text Processing and Multiclass Classification Chevron down icon Chevron up icon
Chapter 9: Text Processing and Multiclass Classification Technical requirements Introduction to text processing Text vectorization techniques Feature extraction from text Implementing text classification models Multiclass classification strategies Evaluating text models Practical exercises in text processing
Chapter 10: Clustering Techniques Chevron down icon Chevron up icon
Chapter 10: Clustering Techniques Technical requirements Introduction to clustering K-means clustering Hierarchical clustering Density-based clustering with DBSCAN Cluster evaluation metrics Choosing the right clustering algorithm Advanced clustering techniques Practical exercises with clustering models
Chapter 11: Novelty and Outlier Detection Chevron down icon Chevron up icon
Chapter 11: Novelty and Outlier Detection Technical requirements Introduction to outlier and novelty detection Understanding Isolation Forest One-Class SVM for novelty detection Detecting outliers with LOF Evaluating outlier detection models Handling detected outliers Choosing the right detection technique Practical exercises in novelty and outlier detection
Chapter 12: Cross-Validation and Model Evaluation Techniques Chevron down icon Chevron up icon
Chapter 12: Cross-Validation and Model Evaluation Techniques Technical requirements Introduction to cross-validation Advanced cross-validation methods Implementing cross-validation in scikit-learn Model selection techniques Evaluating model generalizability Practical exercises in cross-validation and evaluation
Chapter 13: Deploying scikit-learn Models in Production Chevron down icon Chevron up icon
Chapter 13: Deploying scikit-learn Models in Production Technical requirements Overview of model deployment Serialization and persistence techniques Scaling models for production Monitoring and updating deployed models Managing the model life cycle Setting up deployment pipelines Practical exercises in model deployment
Chapter 14: Unlock Your Exclusive Benefits Chevron down icon Chevron up icon
Chapter 14: 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
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30 Agents Every AI Engineer Must Build
30 Agents Every AI Engineer Must Build
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By Imran Ahmad
March 2026 | 323 pages
Icon Get to grips with foundational agent principles including perception, memory, reasoning, and planning
Icon Integrate advanced frameworks like LangChain and AutoGPT in your AI agent development
Icon Design agents using advanced prompting, knowledge retrieval, and multi-agent orchestration
Icon Purchase of the print or Kindle book includes a free PDF eBook
30 Agents Every AI Engineer Must Build: Transform LLMs into autonomous decision-making vertical agents in healthcare, finance, and beyond Chevron down icon Chevron up icon
30 Agents Every AI Engineer Must Build: Transform LLMs into autonomous decision-making vertical agents in healthcare, finance, and beyond
1 Foundations of Agent Engineering Chevron down icon Chevron up icon
Before you begin: Join our GenAI & LLM Community on Discord 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
2The Agent Engineer's Toolkit Chevron down icon Chevron up icon
Before you begin: Join our GenAI & LLM Community on Discord Agent development frameworks: The architect's blueprint Large language models: The cognitive core Supporting infrastructure: The agent ecosystem Cloud-native agent development platforms: The managed arsenal Summary
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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 Full star icon Empty star icon 4
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
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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
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AI Agents in Practice
AI Agents in Practice
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Full star icon Full star icon Full star icon Full star icon Full star icon 5
By Valentina Alto
August 2025 | 282 pages
Icon Build production-ready AI agents with hands-on tutorials for diverse industry applications
Icon Explore multi-agent system architectures with practical frameworks for orchestrator comparison
Icon Future-proof your AI development with ethical implementation strategies and security patterns
Icon Purchase of the print or Kindle book includes a free PDF eBook
Part 1: Foundations of AI Workflows and the Rise of AI Agents Chevron down icon Chevron up icon
Part 1: Foundations of AI Workflows and the Rise of AI Agents
Evolution of GenAI Workflows Chevron down icon Chevron up icon
Evolution of GenAI Workflows Technical requirements Understanding foundation models and the rise of LLMs Latest significant breakthroughs Road to AI agents The need for an additional layer of intelligence: introducing AI agents Summary References
The Rise of AI Agents Chevron down icon Chevron up icon
The Rise of AI Agents Technical requirements Evolution of agents from RPA to AI agents Components of an AI agent Different types of AI agents Summary References Subscribe for a Free eBook
Part 2: Designing, Building, and Scaling AI Agents Chevron down icon Chevron up icon
Part 2: Designing, Building, and Scaling AI Agents
The Need for an AI Orchestrator Chevron down icon Chevron up icon
The Need for an AI Orchestrator Introduction to AI orchestrators Core components of an AI orchestrator Overview of the most popular AI orchestrators in the market How to choose the right orchestrator for your AI agent Summary References
The Need for Memory and Context Management Chevron down icon Chevron up icon
The Need for Memory and Context Management Different types of memory Managing context windows Storing, retrieving, and refreshing memory Popular tools to manage memory Summary References Subscribe for a Free eBook
The Need for Tools and External Integrations Chevron down icon Chevron up icon
The Need for Tools and External Integrations Technical requirements The anatomy of an AI agent’s tools Hardcoded and semantic functions APIs and web services Databases and knowledge bases Synchronous versus asynchronous calls Summary References
Building Your First AI Agent with LangChain Chevron down icon Chevron up icon
Building Your First AI Agent with LangChain Technical requirements Introduction to the LangChain ecosystem Overview of out-of-the-box components Use case – e-commerce AI agent Summary References Subscribe for a Free eBook
Multi-Agent Applications Chevron down icon Chevron up icon
Multi-Agent Applications Technical requirements Introduction to multi-agent systems Understanding and designing different workflows for your multi-agent system Overview of multi-agent orchestrators Building your first multi-agent application with LangGraph Summary References
Part 3: Road to an Open, Agentic Ecosystem Chevron down icon Chevron up icon
Part 3: Road to an Open, Agentic Ecosystem
Orchestrating Intelligence: Blueprint for Next-Gen Agent Protocols Chevron down icon Chevron up icon
Orchestrating Intelligence: Blueprint for Next-Gen Agent Protocols Technical requirements What is a protocol? Understanding the Model Context Protocol Agent2Agent Agent Commerce Protocol Toward an agentic web Summary References Subscribe for a Free eBook
Navigating Ethical Challenges in Real-World AI Chevron down icon Chevron up icon
Navigating Ethical Challenges in Real-World AI Ethical challenges in AI – fairness, transparency, privacy, and accountability Agentic AI autonomy and its unique ethical challenges Guardrails for safe and ethical AI Content filtering and moderation in AI systems Addressing the challenges: governance, regulations, and collaboration Summary References
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Apps and Services with .NET 10
Apps and Services with .NET 10
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By Mark J. Price
February 2026 | 665 pages
Icon Use specialized libraries to improve all aspects of your apps, including security and localization
Icon Unlock scalability, performance, and resilience in your services
Icon Unleash the capabilities of Avalonia, Blazor, and MAUI to develop stunning, truly cross-platform apps for desktop, web, and mobile
Apps and Services with .NET 10, Third Edition: Build practical projects with Avalonia, Blazor, gRPC, GraphQL, and other enterprise technologies Chevron down icon Chevron up icon
Apps and Services with .NET 10, Third Edition: Build practical projects with Avalonia, Blazor, gRPC, GraphQL, and other enterprise technologies
1 Introducing Apps and Services with .NET Chevron down icon Chevron up icon
1 Introducing Apps and Services with .NET Introducing this book and its contents App and service technologies Setting up your development environment Exploring top-level programs, functions, and namespaces Making good use of the GitHub repository for this book Where to go for help Practicing and exploring Summary Learn more on Discord
2 Building Mobile Apps Using .NET MAUI Chevron down icon Chevron up icon
2 Building Mobile Apps Using .NET MAUI Understanding XAML and .NET MAUI Building mobile and desktop apps using .NET MAUI Using shared resources Using data binding Practicing and exploring Summary
3Building Desktop Apps Using Avalonia Chevron down icon Chevron up icon
3Building Desktop Apps Using Avalonia Understanding Avalonia Developer experience Skia graphics engine Working with Avalonia controls Creating an Avalonia project Referencing assets like images Practicing and exploring Summary
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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
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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
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Python Machine Learning By Example
Python Machine Learning By Example
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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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Asynchronous Programming in Python
Asynchronous Programming in Python
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By Nicolas Bohorquez
November 2025 | 202 pages
Icon Understand core principles and theory behind async programming in Python
Icon Measure the impact of async techniques in practical, real-world use cases
Icon Apply async patterns in software design and data-oriented architectures
Synchronous and Asynchronous Programming Paradigms Chevron down icon Chevron up icon
Synchronous and Asynchronous Programming Paradigms Free Benefits with Your Book Technical requirements Understanding synchronous and asynchronous programming Operating system process and threads Green threads, coroutines and fibers Callbacks, futures and promises Challenges of asynchronous programming Summary
Identifying Concurrency and Parallelism Chevron down icon Chevron up icon
Identifying Concurrency and Parallelism Technical requirements Understanding concurrency and parallelism Applying multiprocessing and multithreading Detecting embarrassingly parallel workloads Summary
Generators and Coroutines Chevron down icon Chevron up icon
Generators and Coroutines Technical requirements Understanding generators and coroutines Introducing Python performance benchmarking Using coroutines in asynchronous programming Summary
Implementing Coroutines with Asyncio and Trio Chevron down icon Chevron up icon
Implementing Coroutines with Asyncio and Trio Technical requirements Using asyncio to implement coroutines Exploring Trio Summary
Assessing Common Mistakes in Asynchronous Programming Chevron down icon Chevron up icon
Assessing Common Mistakes in Asynchronous Programming Technical requirements Profiling CPU usage in asynchronous code Detecting common mistakes in asynchronous code Handling exceptions in asynchronous code Summary
Testing and Asynchronous Design Patterns Chevron down icon Chevron up icon
Testing and Asynchronous Design Patterns Technical requirements Testing asynchronous code Working with the half-synchronous/half-asynchronous pattern Implementing the monitor object pattern Using the read-write lock pattern Applying the leader/followers pattern Summary
Asynchronous Programming in Django, Flask and Quart Chevron down icon Chevron up icon
Asynchronous Programming in Django, Flask and Quart Technical requirements Reviewing the MVC pattern Understanding asynchronous capabilities in Flask Discovering a native asynchronous web framework in Quart Reviewing Django asynchronous support Summary
Asynchronous Data Access Chevron down icon Chevron up icon
Asynchronous Data Access Technical requirements Understanding Python’s API for database connectors Making use of in-process databases Accessing external databases Using object-relational mappers Summary
Asynchronous Data Pipelines Chevron down icon Chevron up icon
Asynchronous Data Pipelines Technical requirements Understanding data pipelines Working with data pipelines Scaling data pipelines in cloud environments Summary
Asynchronous Computing with Notebooks Chevron down icon Chevron up icon
Asynchronous Computing with Notebooks Technical requirements Understanding interactive computing Simulating complexity with asynchronous programming Designing user interfaces in notebooks Stabilizing simulations with asynchronous programming 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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AI Agents in Practice
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Feb-26
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Expert reading lists

If you want to advance your tech knowledge but don't know where to start, explore Expert Reading Lists comprising our best titles on popular technologies grouped together by the Packt community.

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Expert Reading List

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Python – Complete Python, Django, Data Science and ML Guide Python for OOP - The A to Z OOP Python Programming Course Mastering Python 2E
Building a Strong Foundation in Python
Explore this list for refining your Python skills and tackling business challenges. Discover techniques for writing clean code and building modern, high-performance, and scalable applications.
Created: Oct-23
15 items in the reading list Icon
Python – Complete Python, Django, Data Science and ML Guide Python – Complete Python, Django, Data Science and ML Guide
Python for OOP - The A to Z OOP Python Programming Course Python for OOP - The A to Z OOP Python Programming Course
Mastering Python 2E Mastering Python 2E
Functional Python Programming, 3rd edition Functional Python Programming, 3rd edition
PEP8 Guidelines: Python Clean Coding PEP8 Guidelines: Python Clean Coding
Complete Python Course with 10 Real-World Projects Complete Python Course with 10 Real-World Projects
Python GUI Development with Tkinter - Build Pro Desktop Apps! Python GUI Development with Tkinter - Build Pro Desktop Apps!
Python 101 - From Noob to Nerd Python 101 - From Noob to Nerd
Object Oriented Python Object Oriented Python
Python Data Model Python Data Model
Introduction to Programming Using Python Introduction to Programming Using Python
Python Programming Essentials Python Programming Essentials
Python Mastery: Build Web Apps, Automate Tasks & Explore AI Python Mastery: Build Web Apps, Automate Tasks & Explore AI
Learn Python Programming Learn Python Programming
Modern Python Cookbook Modern Python Cookbook
Learning C# by Developing Games with Unity Hands-On Unity 2020 Game Development Beginning C++ Game Programming
Getting Started with Game Development
Start your journey in game development by building games using beginner-friendly tools. Learn programming language, explore design basics, and create your first game projects from scratch.
Created: Apr-25
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Learning C# by Developing Games with Unity Learning C# by Developing Games with Unity
Hands-On Unity 2020 Game Development Hands-On Unity 2020 Game Development
Beginning C++ Game Programming Beginning C++ Game Programming
Learning Java by Building Android Games Learning Java by Building Android Games
Learn React Hooks Learn React with TypeScript Modern Full-Stack React Projects
React Web Development
This curated reading list is designed to elevate your web development career. It guides you in crafting contemporary web applications with React and its comprehensive toolkit. Through hands-on learning, you'll unlock React's potential, paving your path toward becoming a proficient React developer. Beyond fundamental concepts, you'll also acquire mastery in React through the exploration of diverse design patterns and industry best practices.
Created: Dec-25
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Learn React Hooks Learn React Hooks
Learn React with TypeScript Learn React with TypeScript
Modern Full-Stack React Projects Modern Full-Stack React Projects
React and React Native React and React Native
React Key Concepts React Key Concepts
Full Stack FastAPI, React, and MongoDB Full Stack FastAPI, React, and MongoDB
Modern Generative AI with ChatGPT and OpenAI Models Coding with ChatGPT and Other LLMs Prompt Engineering For Everyone with ChatGPT and GPT-4
Getting Started with ChatGPT
Stay ahead of the game and start unlocking the full potential of OpenAI and ChatGPT today.
Created: Mar-25
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Modern Generative AI with ChatGPT and OpenAI Models Modern Generative AI with ChatGPT and OpenAI Models
Coding with ChatGPT and Other LLMs Coding with ChatGPT and Other LLMs
Prompt Engineering For Everyone with ChatGPT and GPT-4 Prompt Engineering For Everyone with ChatGPT and GPT-4
The Ultimate ChatGPT Guide with React Native - Develop Mobile Apps The Ultimate ChatGPT Guide with React Native - Develop Mobile Apps
Make Apps with ChatGPT and Generative AI Make Apps with ChatGPT and Generative AI
Building a ChatGPT AI with JavaScript - An OpenAI Code-Along Guide Building a ChatGPT AI with JavaScript - An OpenAI Code-Along Guide
Building AI Applications with ChatGPT APIs Building AI Applications with ChatGPT APIs
ChatGPT for Cybersecurity Cookbook ChatGPT for Cybersecurity Cookbook
ChatGPT for Conversational AI and Chatbots ChatGPT for Conversational AI and Chatbots
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Top 10 New Releases

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Engineering Lakehouses with Open Table Formats Engineering Lakehouses with Open Table Formats
Dec-25
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Open Data Lakehouse: A New Architectural Paradigm Icon Transactional Capabilities of the Lakehouse Icon Apache Iceberg Deep Dive Icon Apache Hudi Deep Dive Icon Delta Lake Deep Dive Icon Catalog and Metadata Management Icon Interoperability in Lakehouses Icon Performance Optimization and Tuning in a Lakehouse Icon Data Governance and Security in Lakehouses Icon Evaluating and Selecting Open Table Formats Icon Real-World Applications and Learnings Icon Unlock Your Exclusive Benefits Icon Other Books You May Enjoy Icon Index Icon
The Rust Programming Handbook The Rust Programming Handbook
Dec-25
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Getting Started with Rust Icon Rust Syntax and Functions Icon Functions in Rust Icon Ownership, Borrowing, and References Icon Composite Types in Rust and the Module System Icon Introduction to Error Handling Icon Polymorphism and Lifetimes Icon Object-Oriented Programming in Rust Icon Thinking Functionally in Rust Icon Testing in Rust Icon Smart Pointers and Memory Management Icon Managing System Resources Icon Concurrency and Parallelism Icon Rust for Web Development: Building Full-Stack Applications Icon System Programming in Rust: Concrete Examples Icon Dockerization and Deployment of Rust Applications Icon Unlock Your Exclusive Benefits Icon Other Books You May Enjoy Icon Index Icon
Software Architecture with C++ Software Architecture with C++
Dec-25
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Concepts and Components of Software Architecture Icon Importance of Software Architecture and Principles of Great Design Icon Architectural Styles Icon Functional and Non-Functional Requirements Icon The Design and Development of C++ Software Icon Architectural and System Design Patterns Icon Leveraging C++ Language Features Icon Design Patterns and C++ Idioms Icon Building and Packaging Icon Package Management Icon The Future of C++ Code Reuse: Using Modules Icon Architectural Quality Attributes Icon Writing Testable Code Icon Continuous Integration and Continuous Deployment Icon Security in Code and Deployment Icon Performance Icon Cloud-Native Design Principles Icon Architecture of Distributed Systems Icon Interservice Communication Icon Containers Icon Observability Icon Cloud-Native Design Icon Unlock Your Book’s Exclusive Benefits Icon Assessments Icon Other Books You May Enjoy Icon Index Icon
Microservices Design Patterns in .NET Microservices Design Patterns in .NET
Dec-25
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Understanding Microservices and Design Patterns Icon Introducing Microservices – the Big Picture Icon Applying Domain-Driven Design Principles Icon Synchronous Communication between Microservices Icon Asynchronous Communication Between Microservices Icon Working with the Aggregator Pattern Icon Working with the CQRS Pattern Icon Applying Event-Sourcing Patterns Icon Database and Storage Design Patterns Icon Database Design Strategies for Microservices Icon Implementing Transactions across Microservices Using the Saga Pattern Icon Resiliency, Security, and Infrastructure Patterns Icon Building Resilient Microservices Icon Implementing the API and BFF Gateway Patterns Icon Micro Frontends — Extending Microservices to the Frontend Icon Securing Microservices Icon Cloud Development Strategies Icon Container Hosting and Development Patterns Icon Serverless Microservices Development Icon Observability and Monitoring with Modern Tools Icon Wrapping It All Up Icon Index Icon Other Books You May Enjoy 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
The Kaggle Book The Kaggle Book
Dec-25
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Part 1: Your Kaggle Launchpad: Mastering the Essentials Icon Introducing Kaggle and Other Data Science Competitions Icon Organizing Data with Datasets Icon Working and Learning with Kaggle Notebooks Icon Kaggle Models Icon Leveraging Discussion Forums Icon Part 2: Elevating Your Game: Advanced Techniques for Competitive Success Icon Competition Tasks and Metrics Icon Designing Good Validation Icon Modeling for Tabular Competitions Icon Hyperparameter Optimization Icon Ensembling with Blending and Stacking Solutions Icon Modeling for Computer Vision Icon Modeling for NLP Icon Generative AI in Kaggle Competitions Icon Simulation and Optimization Competitions Icon Part 3: Kaggle for Your Career: Building Your Profile and Finding Opportunities Icon Creating Your Portfolio of Projects and Ideas Icon Finding New Professional Opportunities Icon Unlock Your Exclusive Benefits Icon Other Books You May Enjoy Icon Index Icon
Mastering Adobe Photoshop 2026 Mastering Adobe Photoshop 2026
Dec-25
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Part 1: Raising Your Photoshop Game Icon What’s New in Photoshop 2026? Icon Making Photoshop Work Harder and Smarter Icon Non-Destructive Healing and Retouching Icon Masking and Cutouts Icon Part 2: Bringing Brands Front and Center Icon Building Brand Mock-Ups and Prototypes Icon Creating Printed Marketing Collateral Icon From Pixels to Post: Images for Social Media Icon Creating Animated GIFs and Videos Icon Part 3: Building Visual Components Icon Bringing Typography to Life with Effects and Styling Icon Creating Textures, Patterns, and Backdrops Icon Creating and Applying Brushes Icon Part 4: World Building Icon Integrating Artificial Intelligence Icon Blending and Collaging Images Icon Creating Surrealist Artwork Icon Unlock Your Exclusive Benefits Icon Other Books You May Enjoy Icon Index Icon
Mastering Go for DevOps Mastering Go for DevOps
Dec-25
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Part 1: Go Foundations - CLI, Monitoring, and APIs Icon Developing Command-Line Interfaces with Go Icon Packaging and Distributing Go CLIs Icon Integrating Go Applications with Prometheus Icon Writing Go Exporters for Prometheus Icon Building and Consuming RESTful APIs with Go Icon Working with gRPC and Microservices Architecture Icon Part 2: Build Custom Terraform Providers with Go Icon Using Go to Build Custom Terraform Providers Icon Writing Unit Tests and Integration Tests for Terraform Providers Icon Documenting and Publishing Terraform Providers Icon Automating Testing in Pipelines Icon Part 3: Go for Cloud Services Icon Integrating Go Applications with the AWS SDK Icon Integrating Go Applications with the Azure SDK Icon Serverless Computing Using AWS Lambda Icon Serverless Computing Using Azure Functions Icon Unlock Your Exclusive Benefits Icon Other Books You May Enjoy Icon Index Icon
AI-Native LLM Security AI-Native LLM Security
Dec-25
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Fundamentals and Introduction to Large Language Models Icon Securing Large Language Models Icon The Dual Nature of LLM Risks: Inherent Vulnerabilities and Malicious Actors Icon Mapping Trust Boundaries in LLM Architectures Icon Aligning LLM Security with Organizational Objectives and Regulatory Landscapes Icon Identifying and Prioritizing LLM Security Risks with OWASP Icon Diving Deep: Profiles of the Top 10 LLM Security Risks Icon Mitigating LLM Risks: Strategies and Techniques for Each OWASP Category Icon Adapting the OWASP Top 10 to Diverse Deployment Scenarios Icon Designing LLM Systems for Security: Architecture, Controls, and Best Practices Icon Integrating Security into the LLM Development Life Cycle: From Data Curation to Deployment Icon Operational Resilience: Monitoring, Incident Response, and Continuous Improvement Icon The Future of LLM Security: Emerging Threats, Promising Defenses, and the Path Forward Icon Appendix A Icon Appendix B Icon
The Azure Cloud Native Architecture Mapbook The Azure Cloud Native Architecture Mapbook
Dec-25
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Part 1: Building the Foundations Icon Getting Started as an Azure Architect Icon Solution Architecture Icon Infrastructure Design Icon Working with Azure Kubernetes Service (AKS) Icon Working with Other Container Services Icon Developing and Designing Applications with Azure Icon Part 2: Designing Intelligent and Secure Architectures Icon Data Architecture Icon Artificial Intelligence Architecture Icon Security Architecture Icon Closing Thoughts and Next Steps Icon Unlock Your Exclusive Benefits Icon Other Books You May Enjoy Icon Index Icon

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Mathematics of Machine Learning Mathematics of Machine Learning
May-25
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Introduction Icon Part 1: Linear Algebra Icon 1 Vectors and Vector Spaces Icon 2 The Geometric Structure of Vector Spaces Icon 3 Linear Algebra in Practice Icon 4 Linear Transformations Icon 5 Matrices and Equations Icon 6 Eigenvalues and Eigenvectors Icon 7 Matrix Factorizations Icon 8 Matrices and Graphs Icon References Icon Part 2: Calculus Icon 9 Functions Icon 10 Numbers, Sequences, and Series Icon 11 Topology, Limits, and Continuity Icon 12 Differentiation Icon 13 Optimization Icon 14 Integration Icon References Icon Part 3: Multivariable Calculus Icon 15 Multivariable Functions Icon 16 Derivatives and Gradients Icon 17 Optimization in Multiple Variables Icon References Icon Part 4: Probability Theory Icon 18 What is Probability? Icon 19 Random Variables and Distributions Icon 20 The Expected Value Icon References Icon Part 5: Appendix Icon Other Books You May Enjoy Icon Index Icon
Building Agentic AI Systems Building Agentic AI Systems
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Part 1: Foundations of Generative AI and Agentic Systems Icon Chapter 1: Fundamentals of Generative AI Icon Chapter 2: Principles of Agentic Systems Icon Chapter 3: Essential Components of Intelligent Agents Icon Part 2: Designing and Implementing Generative AI-Based Agents Icon Chapter 4: Reflection and Introspection in Agents Icon Chapter 5: Enabling Tool Use and Planning in Agents Icon Chapter 6: Exploring the Coordinator, Worker, and Delegator Approach Icon Chapter 7: Effective Agentic System Design Techniques Icon Part 3: Trust, Safety, Ethics, and Applications Icon Chapter 8: Building Trust in Generative AI Systems Icon Chapter 9: Managing Safety and Ethical Considerations Icon Chapter 10: Common Use Cases and Applications Icon Chapter 11: Conclusion and Future Outlook Icon Chapter 12: Unlock Your Exclusive Benefits Icon Index Icon Other Books You May Enjoy Icon
Building AI Agents with LLMs, RAG, and Knowledge Graphs Building AI Agents with LLMs, RAG, and Knowledge Graphs
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Part 1: The AI Agent Engine: From Text to Large Language Models Icon Chapter 1: Analyzing Text Data with Deep Learning Icon Chapter 2: The Transformer: The Model Behind the Modern AI Revolution Icon Chapter 3: Exploring LLMs as a Powerful AI Engine Icon Part 2: AI Agents and Retrieval of Knowledge Icon Chapter 4: Building a Web Scraping Agent with an LLM Icon Chapter 5: Extending Your Agent with RAG to Prevent Hallucinations Icon Chapter 6: Advanced RAG Techniques for Information Retrieval and Augmentation Icon Chapter 7: Creating and Connecting a Knowledge Graph to an AI Agent Icon Chapter 8: Reinforcement Learning and AI Agents Icon Part 3: Creating Sophisticated AI to Solve Complex Scenarios Icon Chapter 9: Creating Single- and Multi-Agent Systems Icon Chapter 10: Building an AI Agent Application Icon Chapter 11: The Future Ahead Icon Chapter 12: Unlock Your Exclusive Benefits Icon Index Icon Other Books You May Enjoy Icon
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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
Building Agents with OpenAI Agents SDK Building Agents with OpenAI Agents SDK
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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
C# 14 and .NET 10 – Modern Cross-Platform Development Fundamentals C# 14 and .NET 10 – Modern Cross-Platform Development Fundamentals
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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
AI Agents in Practice AI Agents in Practice
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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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The Node.js Platform Icon The Module System Icon Callbacks and Events Icon Asynchronous Control Flow Patterns with Callbacks Icon Asynchronous Control Flow Patterns with Promises and Async/Await Icon Coding with Streams Icon Creational Design Patterns Icon Structural Design Patterns Icon Behavioral Design Patterns Icon Testing: Patterns and Best Practices Icon Advanced Recipes Icon Scalability and Architectural Patterns Icon Messaging and Integration Patterns Icon Other Books You May Enjoy Icon Index Icon
Generative AI with LangChain Generative AI with LangChain
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The Rise of Generative AI: From Language Models to Agents Icon First Steps with LangChain Icon Building Workflows with LangGraph Icon Building Intelligent RAG Systems Icon Building Intelligent Agents Icon Advanced Applications and Multi-Agent Systems Icon Software Development and Data Analysis Agents Icon Evaluation and Testing Icon Production-Ready LLM Deployment and Observability Icon The Future of Generative Models: Beyond Scaling Icon Other Books You May Enjoy Icon Index Icon

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