Artificial intelligence with Python: build real-world artifical intelligence applications with Python to intelligently interact with the world around you
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| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Birmingham ; Mumbai
Packt Publishing
January 2017
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| Subjects: | |
| Online Access: | FHI01 FWS01 FWS02 UBY01 UER01 Volltext Inhaltsverzeichnis |
| Physical Description: | 1 Online-Ressource (vi, 422 Seiten) Illustrationen, Diagramme (überwiegend farbig) |
| ISBN: | 9781786469670 |
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| adam_text | Artificial Intelligence with
Python
Build real-world Artificial Intelligence applications with Python
to intelligently interact with the world around you
Prateek Joshi
BIRMINGHAM - MUMBAI
ULB Darmstadt
lllllllllllllllllll
19698327
Table of Contents
Preface________________________________________________________________________i
Chapter 1: Introduction to Artificial Intelligence_____________________________7
What is Artificial Intelligence? 8
Why do we need to study AI? 8
Applications of AI 12
Branches of AI 14
Defining intelligence using Turing Test 16
Making machines think like humans 18
Building rational agents 20
General Problem Solver 21
Solving a problem with GPS 22
Building an intelligent agent 22
Types of models 24
Installing Python 3 24
Installing on Ubuntu 25
Installing on Mac OS X 25
Installing on Windows 26
Installing packages 26
Loading data 27
Summary 29
Chapter 2: Classification and Regression Using Supervised Learning 31
Supervised versus unsupervised learning 31
What is classification? 32
Preprocessing data 33
Binarization 33
Mean removal 34
Scaling 35
Normalization 36
Label encoding 37
Logistic Regression classifier 38
Naive Bayes classifier 43
Confusion matrix 47
Support Vector Machines 50
Classifying income data using Support Vector Machines 52
What is Regression? 55
Building a single variable regressor 56
Building a multivariable regressor 59
Estimating housing prices using a Support Vector Regressor 61
Summary 63
Chapter 3: Predictive Analytics with Ensemble Learning____________________65
What is Ensemble Learning? 65
Building learning models with Ensemble Learning 66
What are Decision Trees? 66
Building a Decision Tree classifier 67
What are Random Forests and Extremely Random Forests? 72
Building Random Forest and Extremely Random Forest classifiers 72
Estimating the confidence measure of the predictions 78
Dealing with class imbalance 82
Finding optimal training parameters using grid search 89
Computing relative feature importance 92
Predicting traffic using Extremely Random Forest regressor 95
Summary 98
Chapter 4: Detecting Patterns with Unsupervised Learning_________________99
What is unsupervised learning? 99
Clustering data with K-Means algorithm 100
Estimating the number of clusters with Mean Shift algorithm 106
Estimating the quality of clustering with silhouette scores 109
What are Gaussian Mixture Models? 114
Building a classifier based on Gaussian Mixture Models 115
Finding subgroups in stock market using Affinity Propagation model 120
Segmenting the market based on shopping patterns 122
Summary 126
Chapter 5: Building Recommender Systems_________________________________127
Creating a training pipeline 127
Extracting the nearest neighbors 130
Building a K-Nearest Neighbors classifier 134
Computing similarity scores 141
Finding similar users using collaborative filtering 145
Building a movie recommendation system 148
Summary 151
Chapter 6: Logic Programming____________________________________________153
[ii]
What is logic programming? 153
Understanding the building blocks of logic programming 156
Solving problems using logic programming 156
Installing Python packages 157
Matching mathematical expressions 157
Validating primes 159
Parsing a family tree 161
Analyzing geography 167
Building a puzzle solver 170
Summary 174
Chapter 7: Heuristic Search Techniques___________________________________175
What is heuristic search? 175
Uninformed versus Informed search 176
Constraint Satisfaction Problems 177
Local search techniques 177
Simulated Annealing 178
Constructing a string using greedy search 179
Solving a problem with constraints 183
Solving the region-coloring problem 186
Building an 8-puzzle solver 189
Building a maze solver 194
Summary 199
Chapter 8: Genetic Algorithms____________________________________________201
Understanding evolutionary and genetic algorithms 201
Fundamental concepts in genetic algorithms 202
Generating a bit pattern with predefined parameters 203
Visualizing the evolution 210
Solving the symbol regression problem 219
Building an intelligent robot controller 224
Summary 231
Chapter 9: Building Games With Artificial Intelligence__________________ 233
Using search algorithms in games 234
Combinatorial search 234
Minimax algorithm 235
Alpha-Beta pruning 235
Negamax algorithm 236
Installing easyAI library 236
Building a bot to play Last Coin Standing 237
[iiil
Building a bot to play Tic-Tac-Toe 241
Building two bots to play Connect Four™ against each other 244
Building two bots to play Hexapawn against each other 248
Summary 252
Chapter 10: Natural Language Processing__________________________________253
Introduction and installation of packages 253
Tokenizing text data 255
Converting words to their base forms using stemming 256
Converting words to their base forms using lemmatization 258
Dividing text data into chunks 260
Extracting the frequency of terms using a Bag of Words model 262
Building a category predictor 265
Constructing a gender identifier 268
Building a sentiment analyzer 271
Topic modeling using Latent Dirichlet Allocation 275
Summary 278
Chapter 11: Probabilistic Reasoning for Sequential Data__________________279
Understanding sequential data 279
Handling time-series data with Pandas 280
Slicing time-series data 283
Operating on time-series data 285
Extracting statistics from time-series data 288
Generating data using Hidden Markov Models 292
Identifying alphabet sequences with Conditional Random Fields 295
Stock market analysis 300
Summary 303
Chapter 12: Building A Speech Recognizer_________________________________305
Working with speech signals 305
Visualizing audio signals 306
Transforming audio signals to the frequency domain 309
Generating audio signals 311
Synthesizing tones to generate music 314
Extracting speech features 316
Recognizing spoken words 320
Summary 326
Chapter 13: Object Detection and Tracking________________________________327
Installing OpenCV 328
[iv]
Frame differencing 328
Tracking objects using colorspaces 331
Object tracking using background subtraction 335
Building an interactive object tracker using the CAMShift algorithm 339
Optical flow based tracking 347
Face detection and tracking 354
Using Haar cascades for object detection 354
Using integral images for feature extraction 355
Eye detection and tracking 358
Summary 361
Chapter 14: Artificial Neural Networks____________________________________363
Introduction to artificial neural networks 363
Building a neural network 364
Training a neural network 364
Building a Perceptron based classifier 365
Constructing a single layer neural network 369
Constructing a multilayer neural network 373
Building a vector quantizer 378
Analyzing sequential data using recurrent neural networks 381
Visualizing characters in an Optical Character Recognition database 385
Building an Optical Character Recognition engine 388
Summary 391
Chapter 15: Reinforcement Learning________________________________________393
Understanding the premise 393
Reinforcement learning versus supervised learning 394
Real world examples of reinforcement learning 395
Building blocks of reinforcement learning 396
Creating an environment 397
Building a learning agent 402
Summary 406
Chapter 16: Deep Learning with Convolutional Neural Networks______________407
What are Convolutional Neural Networks? 407
Architecture of CNNs 408
Types of layers in a CNN 409
Building a perceptron-based linear regressor 410
Building an image classifier using a single layer neural network 416
Building an image classifier using a Convolutional Neural Network 418
Summary 424
|
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| author | Joshi, Prateek |
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| discipline | Informatik |
| format | Electronic eBook |
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| spellingShingle | Joshi, Prateek Artificial intelligence with Python build real-world artifical intelligence applications with Python to intelligently interact with the world around you Python (Computer program language) Python Programmiersprache (DE-588)4434275-5 gnd Python 3.x (DE-588)7692360-5 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
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| title | Artificial intelligence with Python build real-world artifical intelligence applications with Python to intelligently interact with the world around you |
| title_auth | Artificial intelligence with Python build real-world artifical intelligence applications with Python to intelligently interact with the world around you |
| title_exact_search | Artificial intelligence with Python build real-world artifical intelligence applications with Python to intelligently interact with the world around you |
| title_full | Artificial intelligence with Python build real-world artifical intelligence applications with Python to intelligently interact with the world around you Prateek Joshi |
| title_fullStr | Artificial intelligence with Python build real-world artifical intelligence applications with Python to intelligently interact with the world around you Prateek Joshi |
| title_full_unstemmed | Artificial intelligence with Python build real-world artifical intelligence applications with Python to intelligently interact with the world around you Prateek Joshi |
| title_short | Artificial intelligence with Python |
| title_sort | artificial intelligence with python build real world artifical intelligence applications with python to intelligently interact with the world around you |
| title_sub | build real-world artifical intelligence applications with Python to intelligently interact with the world around you |
| topic | Python (Computer program language) Python Programmiersprache (DE-588)4434275-5 gnd Python 3.x (DE-588)7692360-5 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| topic_facet | Python (Computer program language) Python Programmiersprache Python 3.x Maschinelles Lernen Künstliche Intelligenz |
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