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  • Book Overview & Buying Artificial Intelligence with Python
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Artificial Intelligence with Python

Artificial Intelligence with Python - Second Edition

By : Alberto Artasanchez, Joshi
3.9 (20)
Buy this Book
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Artificial Intelligence with Python

Artificial Intelligence with Python

3.9 (20)
By: Alberto Artasanchez, Joshi
Buy this Book

Overview of this book

Artificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications. This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data. Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques.
Table of Contents (26 chapters)
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Preface
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Preface
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Who this book is for
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What this book covers
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What you need for this book
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Get in touch
1
Introduction to Artificial Intelligence
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Introduction to Artificial Intelligence
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What is AI?
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Why do we need to study AI?
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Branches of AI
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The five tribes of machine learning
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Defining intelligence using the Turing test
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Making machines think like humans
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Building rational agents
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General Problem Solver
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Building an intelligent agent
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Installing Python 3
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Installing packages
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Loading data
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Summary
Lock Free Chapter
2
Fundamental Use Cases for Artificial Intelligence
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Fundamental Use Cases for Artificial Intelligence
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Representative AI use cases
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Digital personal assistants and chatbots
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Personal chauffeur
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Shipping and warehouse management
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Human health
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Knowledge search
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Recommendation systems
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The smart home
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Gaming
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Movie making
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Underwriting and deal analysis
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Data cleansing and transformation
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Summary
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References
3
Machine Learning Pipelines
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Machine Learning Pipelines
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What is a machine learning pipeline?
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Problem definition
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Data ingestion
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Data preparation
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Data segregation
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Model training
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Summary
4
Feature Selection and Feature Engineering
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Feature Selection and Feature Engineering
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Feature selection
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Feature engineering
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Outlier management
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One-hot encoding
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Log transform
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Scaling
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Date manipulation
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Summary
5
Classification and Regression Using Supervised Learning
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Classification and Regression Using Supervised Learning
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Supervised versus unsupervised learning
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What is classification?
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Preprocessing data
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Label encoding
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Logistic regression classifiers
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The Naïve Bayes classifier
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Confusion matrixes
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Support Vector Machines
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Classifying income data using Support Vector Machines
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What is regression?
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Building a single-variable regressor
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Building a multivariable regressor
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Estimating housing prices using a Support Vector Regressor
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Summary
6
Predictive Analytics with Ensemble Learning
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Predictive Analytics with Ensemble Learning
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What are decision trees?
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What is ensemble learning?
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What are random forests and extremely random forests?
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Dealing with class imbalance
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Finding optimal training parameters using grid search
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Computing relative feature importance
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Predicting traffic using an extremely random forest regressor
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Summary
7
Detecting Patterns with Unsupervised Learning
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Detecting Patterns with Unsupervised Learning
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What is unsupervised learning?
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Clustering data with the K-Means algorithm
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What are Gaussian Mixture Models?
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Finding subgroups in stock market using the Affinity Propagation model
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Segmenting the market based on shopping patterns
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Summary
8
Building Recommender Systems
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Building Recommender Systems
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Extracting the nearest neighbors
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Building a K-nearest neighbors classifier
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Computing similarity scores
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Finding similar users using collaborative filtering
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Building a movie recommendation system
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Summary
9
Logic Programming
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Logic Programming
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What is logic programming?
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Understanding the building blocks of logic programming
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Solving problems using logic programming
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Installing Python packages
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Matching mathematical expressions
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Validating primes
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Parsing a family tree
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Analyzing geography
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Building a puzzle solver
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Summary
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Heuristic Search Techniques
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Heuristic Search Techniques
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Is heuristic search artificial intelligence?
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What is heuristic search?
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Constraint satisfaction problems
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Local search techniques
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Constructing a string using greedy search
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Solving a problem with constraints
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Solving the region-coloring problem
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Building an 8-puzzle solver
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Building a maze solver
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Summary
11
Genetic Algorithms and Genetic Programming
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Genetic Algorithms and Genetic Programming
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The evolutionists tribe
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Understanding evolutionary and genetic algorithms
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Fundamental concepts in genetic algorithms
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Generating a bit pattern with predefined parameters
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Visualizing the evolution
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Solving the symbol regression problem
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Building an intelligent robot controller
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Genetic programming use cases
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Summary
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References
12
Artificial Intelligence on the Cloud
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Artificial Intelligence on the Cloud
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Why are companies migrating to the cloud?
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The top cloud providers
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Amazon Web Services (AWS)
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Microsoft Azure
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Google Cloud Platform (GCP)
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Summary
13
Building Games with Artificial Intelligence
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Building Games with Artificial Intelligence
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Using search algorithms in games
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Combinatorial search
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Installing the easyAI library
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Building a bot to play Last Coin Standing
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Building a bot to play Tic-Tac-Toe
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Building two bots to play Connect Four™ against each other
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Building two bots to play Hexapawn against each other
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Summary
14
Building a Speech Recognizer
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Building a Speech Recognizer
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Working with speech signals
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Visualizing audio signals
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Transforming audio signals to the frequency domain
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Generating audio signals
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Synthesizing tones to generate music
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Extracting speech features
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Recognizing spoken words
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Summary
15
Natural Language Processing
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Natural Language Processing
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Introduction and installation of packages
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Tokenizing text data
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Converting words to their base forms using stemming
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Converting words to their base forms using lemmatization
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Dividing text data into chunks
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Extracting the frequency of terms using the Bag of Words model
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Building a category predictor
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Constructing a gender identifier
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Building a sentiment analyzer
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Topic modeling using Latent Dirichlet Allocation
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Summary
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Chatbots
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Chatbots
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The future of chatbots
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Chatbots today
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Chatbot concepts
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A well-architected chatbot
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Chatbot platforms
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Creating a chatbot using DialogFlow
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Summary
17
Sequential Data and Time Series Analysis
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Sequential Data and Time Series Analysis
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Understanding sequential data
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Handling time series data with Pandas
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Slicing time series data
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Operating on time series data
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Extracting statistics from time series data
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Generating data using Hidden Markov Models
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Identifying alphabet sequences with Conditional Random Fields
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Stock market analysis
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Summary
18
Image Recognition
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Image Recognition
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Importance of image recognition
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OpenCV
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Frame differencing
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Tracking objects using color spaces
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Object tracking using background subtraction
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Building an interactive object tracker using the CAMShift algorithm
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Optical flow-based tracking
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Face detection and tracking
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Eye detection and tracking
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Summary
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Neural Networks
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Neural Networks
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Introduction to neural networks
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Building a Perceptron-based classifier
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Constructing a single-layer neural network
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Constructing a multi-layer neural network
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Building a vector quantizer
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Analyzing sequential data using recurrent neural networks
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Visualizing characters in an optical character recognition database
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Building an optical character recognition engine
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Summary
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Deep Learning with Convolutional Neural Networks
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Deep Learning with Convolutional Neural Networks
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The basics of Convolutional Neural Networks
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Architecture of CNNs
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Types of layers in a CNN
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Building a perceptron-based linear regressor
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Building an image classifier using a single-layer neural network
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Building an image classifier using a Convolutional Neural Network
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Summary
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Reference
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Recurrent Neural Networks and Other Deep Learning Models
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Recurrent Neural Networks and Other Deep Learning Models
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The basics of Recurrent Neural Networks
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Architecture of RNNs
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A language modeling use case
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Training an RNN
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Summary
22
Creating Intelligent Agents with Reinforcement Learning
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Creating Intelligent Agents with Reinforcement Learning
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Understanding what it means to learn
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Reinforcement learning versus supervised learning
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Real-world examples of reinforcement learning
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Building blocks of reinforcement learning
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Creating an environment
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Building a learning agent
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Summary
23
Artificial Intelligence and Big Data
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Artificial Intelligence and Big Data
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Big data basics
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The three V's of big data
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Big data and machine learning
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NoSQL Databases
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Summary
24
Other Books You May Enjoy
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Other Books You May Enjoy
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Index
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Index

Generating data using Hidden Markov Models

A Hidden Markov Model (HMM) is a powerful analysis technique for analyzing sequential data. It assumes that the system being modeled is a Markov process with hidden states. This means that the underlying system can be one among a set of possible states.

It goes through a sequence of state transitions, thereby producing a sequence of outputs. We can only observe the outputs but not the states. Hence these states are hidden from us. Our goal is to model the data so that we can infer the state transitions of unknown data.

In order to understand HMMs, let's consider a version of the traveling salesman problem (TSP). In this example, a salesman must travel between the following three cities for his job — London, Barcelona, and New York. His goal is to minimize the traveling time so that he can be the most efficient. Considering his work commitments and schedule, we have a set of probabilities that dictate the chances of ...

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Artificial Intelligence with Python
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