Artificial intelligence with python kho tài liệu bách khoa

Table of Contents Artificial Intelligence with Python Credits About the Author About the Reviewer www.PacktPub.com Why subscribe? Customer Feedback Preface What this book covers What you need for this book Who this book is for Conventions Reader feedback Customer support Downloading the example code Downloading the color images of this book Errata Piracy Questions Introduction to Artificial Intelligence What is Artificial Intelligence? Why do we need to study AI? Applications of AI Branches of AI Defining intelligence using Turing Test Making machines think like humans Building rational agents General Problem Solver Solving a problem with GPS Building an intelligent agent Types of models Installing Python 3 Installing on Ubuntu Installing on Mac OS X Installing on Windows Installing packages Loading data Summary Classification and Regression Using Supervised Learning Supervised versus unsupervised learning What is classification? Preprocessing data Binarization Mean removal Scaling Normalization Label encoding Logistic Regression classifier Naïve Bayes classifier Confusion matrix Support Vector Machines Classifying income data using Support Vector Machines What is Regression? Building a single variable regressor Building a multivariable regressor Estimating housing prices using a Support Vector Regressor Summary Predictive Analytics with Ensemble Learning What is Ensemble Learning? Building learning models with Ensemble Learning What are Decision Trees? Building a Decision Tree classifier What are Random Forests and Extremely Random Forests? Building Random Forest and Extremely Random Forest classifiers Estimating the confidence measure of the predictions Dealing with class imbalance Finding optimal training parameters using grid search Computing relative feature importance Predicting traffic using Extremely Random Forest regressor Summary Detecting Patterns with Unsupervised Learning What is unsupervised learning? Clustering data with K-Means algorithm Estimating the number of clusters with Mean Shift algorithm Estimating the quality of clustering with silhouette scores What are Gaussian Mixture Models? Building a classifier based on Gaussian Mixture Models Finding subgroups in stock market using Affinity Propagation model Segmenting the market based on shopping patterns Summary Building Recommender Systems Creating a training pipeline Extracting the nearest neighbors Building a K-Nearest Neighbors classifier Computing similarity scores Finding similar users using collaborative filtering Building a movie recommendation system Summary Logic Programming What is logic programming? Understanding the building blocks of logic programming Solving problems using logic programming Installing Python packages Matching mathematical expressions Validating primes Parsing a family tree Analyzing geography Building a puzzle solver Summary Heuristic Search Techniques What is heuristic search? Uninformed versus Informed search Constraint Satisfaction Problems Local search techniques Simulated Annealing Constructing a string using greedy search Solving a problem with constraints Solving the region-coloring problem Building an 8-puzzle solver Building a maze solver Summary Genetic Algorithms Understanding evolutionary and genetic algorithms Fundamental concepts in genetic algorithms Generating a bit pattern with predefined parameters Visualizing the evolution Solving the symbol regression problem Building an intelligent robot controller Summary Building Games With Artificial Intelligence Using search algorithms in games Combinatorial search Minimax algorithm Alpha-Beta pruning Negamax algorithm Installing easyAI library Building a bot to play Last Coin Standing Building a bot to play Tic-Tac-Toe Building two bots to play Connect Four™ against each other Building two bots to play Hexapawn against each other Summary 10 Natural Language Processing Introduction and installation of packages Tokenizing text data Converting words to their base forms using stemming Converting words to their base forms using lemmatization Dividing text data into chunks Extracting the frequency of terms using a Bag of Words model Building a category predictor Constructing a gender identifier Building a sentiment analyzer Topic modeling using Latent Dirichlet Allocation Summary 11 Probabilistic Reasoning for Sequential Data Understanding sequential data Handling time-series data with Pandas Slicing time-series data Operating on time-series data Extracting statistics from time-series data Generating data using Hidden Markov Models Identifying alphabet sequences with Conditional Random Fields Stock market analysis Summary 12 Building A Speech Recognizer Working with speech signals Visualizing audio signals Transforming audio signals to the frequency domain Generating audio signals Synthesizing tones to generate music Extracting speech features Recognizing spoken words Summary 13 Object Detection and Tracking Installing OpenCV Frame differencing Tracking objects using colorspaces Object tracking using background subtraction Building an interactive object tracker using the CAMShift algorithm Optical flow based tracking Face detection and tracking Using Haar cascades for object detection Using integral images for feature extraction Eye detection and tracking Summary 14 Artificial Neural Networks Introduction to artificial neural networks Building a neural network Training a neural network Building a Perceptron based classifier Constructing a single layer neural network Constructing a multilayer neural network Building a vector quantizer Analyzing sequential data using recurrent neural networks Visualizing characters in an Optical Character Recognition database Building an Optical Character Recognition engine Summary 15 Reinforcement Learning Understanding the premise Reinforcement learning versus supervised learning Real world examples of reinforcement learning Building blocks of reinforcement learning Creating an environment Building a learning agent Summary 16 Deep Learning with Convolutional Neural Networks What are Convolutional Neural Networks? Architecture of CNNs Types of layers in a CNN Building a perceptron-based linear regressor Building an image classifier using a single layer neural network Building an image classifier using a Convolutional Neural Network Summary Artificial Intelligence with Python Artificial Intelligence with Python Copyright © 2017 Packt Publishing All rights reserved No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews Every effort has been made in the preparation of this book to ensure the accuracy of the information presented However, the information contained in this book is sold without warranty, either express or implied Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals However, Packt Publishing cannot guarantee the accuracy of this information First published: January 2017 Production reference: 1230117 Published by Packt Publishing Ltd Livery Place 35 Livery Street Birmingham B3 2PB, UK ISBN 978-1-78646-439-2 www.packtpub.com Credits Author Prateek Joshi Reviewer Richard Marsden Commissioning Editor Veena Pagare Acquisition Editor Tushar Gupta Content Development Editor Aishwarya Pandere Technical Editor Karan Thakkar Copy Editors Vikrant Phadkay Safis Editing Project Coordinator Nidhi Joshi Proofreader Safis Editing Indexer Mariammal Chettiyar Production Coordinator Shantanu N Zagade About the Author Prateek Joshi is an artificial intelligence researcher, published author of five books, and TEDx speaker He is the founder of Pluto AI, a venture-funded Silicon Valley startup building an analytics platform for smart water management powered by deep learning His work in this field has led to patents, tech demos, and research papers at major IEEE conferences He has been an invited speaker at technology and entrepreneurship conferences including TEDx, AT&T Foundry, Silicon Valley Deep Learning, and Open Silicon Valley Prateek has also been featured as a guest author in prominent tech magazines His tech blog (www.prateekjoshi.com) has received more than 1.2 million page views from 200 over countries and has over 6,600+ followers He frequently writes on topics such as artificial intelligence, Python programming, and abstract mathematics He is an avid coder and has won many hackathons utilizing a wide variety of technologies He graduated from University of Southern California with a master’s degree specializing in artificial intelligence He has worked at companies such as Nvidia and Microsoft Research You can learn more about him on his personal website at www.prateekj.com ... Building an image classifier using a Convolutional Neural Network Summary Artificial Intelligence with Python Artificial Intelligence with Python Copyright © 2017 Packt Publishing All rights reserved No part of this book may be reproduced, stored in a retrieval system, or... This book is for Python developers who want to build real-world artificial intelligence applications This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code... then use these concepts to solve the symbol regression problem and build an intelligent robot controller Chapter 9 , Building Games with Artificial Intelligence , teaches you how to build games with artificial intelligence We will learn how to build various games including Tic Tac Toe, Connect