In Practical Artificial Intelligence Programming with Python: From Zero to Hero, veteran educator and photophysicist Dr. Perry Xiao delivers a thorough introduction to one of the most exciting areas of computer science in modern history. The book demystifies artificial intelligence and teaches readers its fundamentals from scratch in simple and plain language and with illustrative code examples.
Divided into three parts, the author explains artificial intelligence generally, machine learning, and deep learning. It tackles a wide variety of useful topics, from classification and regression in machine learning to generative adversarial networks. He also includes: - Fulsome introductions to MATLAB, Python, AI, machine learning, and deep learning - Expansive discussions on supervised and unsupervised machine learning, as well as semi-supervised learning - Practical AI and Python “cheat sheet” quick references
This hands-on AI programming guide is perfect for anyone with a basic knowledge of programming - including familiarity with variables, arrays, loops, if-else statements, and file input and output - who seeks to understand foundational concepts in AI and AI development.
Table of Contents
Preface xxiii
Part I Introduction
Chapter 1 Introduction to AI 3
1.1 What Is AI? 3
1.2 The History of AI 5
1.3 AI Hypes and AI Winters 9
1.4 The Types of AI 11
1.5 Edge AI and Cloud AI 12
1.6 Key Moments of AI 14
1.7 The State of AI 17
1.8 AI Resources 19
1.9 Summary 21
1.10 Chapter Review Questions 22
Chapter 2 AI Development Tools 23
2.1 AI Hardware Tools 23
2.2 AI Software Tools 24
2.3 Introduction to Python 27
2.4 Python Development Environments 30
2.4 Getting Started with Python 34
2.5 AI Datasets 45
2.6 Python AI Frameworks 47
2.7 Summary 49
2.8 Chapter Review Questions 50
Part II Machine Learning and Deep Learning
Chapter 3 Machine Learning 53
3.1 Introduction 53
3.2 Supervised Learning: Classifications 55
Scikit-Learn Datasets 56
Support Vector Machines 56
Naive Bayes 67
Linear Discriminant Analysis 69
Principal Component Analysis 70
Decision Tree 73
Random Forest 76
K-Nearest Neighbors 77
Neural Networks 78
3.3 Supervised Learning: Regressions 80
3.4 Unsupervised Learning 89
K-means Clustering 89
3.5 Semi-supervised Learning 91
3.6 Reinforcement Learning 93
Q-Learning 95
3.7 Ensemble Learning 102
3.8 AutoML 106
3.9 PyCaret 109
3.10 LazyPredict 111
3.11 Summary 115
3.12 Chapter Review Questions 116
Chapter 4 Deep Learning 117
4.1 Introduction 117
4.2 Artificial Neural Networks 120
4.3 Convolutional Neural Networks 125
4.3.1 LeNet, AlexNet, GoogLeNet 129
4.3.2 VGG, ResNet, DenseNet, MobileNet, EffecientNet, and YOLO 140
4.3.3 U-Net 152
4.3.4 AutoEncoder 157
4.3.5 Siamese Neural Networks 161
4.3.6 Capsule Networks 163
4.3.7 CNN Layers Visualization 165
4.4 Recurrent Neural Networks 173
4.4.1 Vanilla RNNs 175
4.4.2 Long-Short Term Memory 176
4.4.3 Natural Language Processing and Python Natural Language Toolkit 183
4.5 Transformers 187
4.5.1 BERT and ALBERT 187
4.5.2 GPT-3 189
4.5.3 Switch Transformers 190
4.6 Graph Neural Networks 191
4.6.1 SuperGLUE 192
4.7 Bayesian Neural Networks 192
4.8 Meta Learning 195
4.9 Summary 197
4.10 Chapter Review Questions 197
Part III AI Applications
Chapter 5 Image Classification 201
5.1 Introduction 201
5.2 Classification with Pre-trained Models 203
5.3 Classification with Custom Trained Models: Transfer Learning 209
5.4 Cancer/Disease Detection 227
5.4.1 Skin Cancer Image Classification 227
5.4.2 Retinopathy Classification 229
5.4.3 Chest X-Ray Classification 230
5.4.5 Brain Tumor MRI Image Classification 231
5.4.5 RSNA Intracranial Hemorrhage Detection 231
5.5 Federated Learning for Image Classification 232
5.6 Web-Based Image Classification 233
5.6.1 Streamlit Image File Classification 234
5.6.2 Streamlit Webcam Image Classification 242
5.6.3 Streamlit from GitHub 248
5.6.4 Streamlit Deployment 249
5.7 Image Processing 250
5.7.1 Image Stitching 250
5.7.2 Image Inpainting 253
5.7.3 Image Coloring 255
5.7.4 Image Super Resolution 256
5.7.5 Gabor Filter 257
5.8 Summary 262
5.9 Chapter Review Questions 263
Chapter 6 Face Detection and Face Recognition 265
6.1 Introduction 265
6.2 Face Detection and Face Landmarks 266
6.3 Face Recognition 279
6.3.1 Face Recognition with Face_Recognition 279
6.3.2 Face Recognition with OpenCV 285
6.3.3 GUI-Based Face Recognition System 288
Other GUI Development Libraries 300
6.3.4 Google FaceNet 301
6.4 Age, Gender, and Emotion Detection 301
6.4.1 DeepFace 302
6.4.2 TCS-HumAIn-2019 305
6.5 Face Swap 309
6.5.1 Face_Recognition and OpenCV 310
6.5.2 Simple_Faceswap 315
6.5.3 DeepFaceLab 322
6.6 Face Detection Web Apps 322
6.7 How to Defeat Face Recognition 334
6.8 Summary 335
6.9 Chapter Review Questions 336
Chapter 7 Object Detections and Image Segmentations 337
7.1 Introduction 337
R-CNN Family 338
YOLO 339
SSD 340
7.2 Object Detections with Pretrained Models 341
7.2.1 Object Detection with OpenCV 341
7.2.2 Object Detection with YOLO 346
7.2.3 Object Detection with OpenCV and Deep Learning 351
7.2.4 Object Detection with TensorFlow, ImageAI, Mask RNN, PixelLib, Gluon 354
TensorFlow Object Detection 354
ImageAI Object Detection 355
MaskRCNN Object Detection 357
Gluon Object Detection 363
7.2.5 Object Detection with Colab OpenCV 364
7.3 Object Detections with Custom Trained Models 369
7.3.1 OpenCV 369
Step 1 369
Step 2 369
Step 3 369
Step 4 370
Step 5 371
7.3.2 YOLO 372
Step 1 372
Step 2 372
Step 3 373
Step 4 375
Step 5 375
7.3.3 TensorFlow, Gluon, and ImageAI 376
TensorFlow 376
Gluon 376
ImageAI 376
7.4 Object Tracking 377
7.4.1 Object Size and Distance Detection 377
7.4.2 Object Tracking with OpenCV 382
Single Object Tracking with OpenCV 382
Multiple Object Tracking with OpenCV 384
7.4.2 Object Tracking with YOLOv4 and DeepSORT 386
7.4.3 Object Tracking with Gluon 389
7.5 Image Segmentation 389
7.5.1 Image Semantic Segmentation and Image Instance Segmentation 390
PexelLib 390
Detectron2 394
Gluon CV 394
7.5.2 K-means Clustering Image Segmentation 394
7.5.3 Watershed Image Segmentation 396
7.6 Background Removal 405
7.6.1 Background Removal with OpenCV 405
7.6.2 Background Removal with PaddlePaddle 423
7.6.3 Background Removal with PixelLib 425
7.7 Depth Estimation 426
7.7.1 Depth Estimation from a Single Image 426
7.7.2 Depth Estimation from Stereo Images 428
7.8 Augmented Reality 430
7.9 Summary 431
7.10 Chapter Review Questions 431
Chapter 8 Pose Detection 433
8.1 Introduction 433
8.2 Hand Gesture Detection 434
8.2.1 OpenCV 434
8.2.2 TensorFlow.js 452
8.3 Sign Language Detection 453
8.4 Body Pose Detection 454
8.4.1 OpenPose 454
8.4.2 OpenCV 455
8.4.3 Gluon 455
8.4.4 PoseNet 456
8.4.5 ML5JS 457
8.4.6 MediaPipe 459
8.5 Human Activity Recognition 461
ActionAI 461
Gluon Action Detection 461
Accelerometer Data HAR 461
8.6 Summary 464
8.7 Chapter Review Questions 464
Chapter 9 GAN and Neural-Style Transfer 465
9.1 Introduction 465
9.2 Generative Adversarial Network 466
9.2.1 CycleGAN 467
9.2.2 StyleGAN 469
9.2.3 Pix2Pix 474
9.2.4 PULSE 475
9.2.5 Image Super-Resolution 475
9.2.6 2D to 3D 478
9.3 Neural-Style Transfer 479
9.4 Adversarial Machine Learning 484
9.5 Music Generation 486
9.6 Summary 489
9.7 Chapter Review Questions 489
Chapter 10 Natural Language Processing 491
10.1 Introduction 491
10.1.1 Natural Language Toolkit 492
10.1.2 spaCy 493
10.1.3 Gensim 493
10.1.4 TextBlob 494
10.2 Text Summarization 494
10.3 Text Sentiment Analysis 508
10.4 Text/Poem Generation 510
10.5.1 Text to Speech 515
10.5.2 Speech to Text 517
10.6 Machine Translation 522
10.7 Optical Character Recognition 523
10.8 QR Code 524
10.9 PDF and DOCX Files 527
10.10 Chatbots and Question Answering 530
10.10.1 ChatterBot 530
10.10.2 Transformers 532
10.10.3 J.A.R.V.I.S. 534
10.10.4 Chatbot Resources and Examples 540
10.11 Summary 541
10.12 Chapter Review Questions 542
Chapter 11 Data Analysis 543
11.1 Introduction 543
11.2 Regression 544
11.2.1 Linear Regression 545
11.2.2 Support Vector Regression 547
11.2.3 Partial Least Squares Regression 554
11.3 Time-Series Analysis 563
11.3.1 Stock Price Data 563
11.3.2 Stock Price Prediction 565
Streamlit Stock Price Web App 569
11.3.4 Seasonal Trend Analysis 573
11.3.5 Sound Analysis 576
11.4 Predictive Maintenance Analysis 580
11.5 Anomaly Detection and Fraud Detection 584
11.5.1 Numenta Anomaly Detection 584
11.5.2 Textile Defect Detection 584
11.5.3 Healthcare Fraud Detection 584
11.5.4 Santander Customer Transaction Prediction 584
11.6 COVID-19 Data Visualization and Analysis 585
11.7 KerasClassifier and KerasRegressor 588
11.7.1 KerasClassifier 589
11.7.2 KerasRegressor 593
11.8 SQL and NoSQL Databases 599
11.9 Immutable Database 608
11.9.1 Immudb 608
11.9.2 Amazon Quantum Ledger Database 609
11.10 Summary 610
11.11 Chapter Review Questions 610
Chapter 12 Advanced AI Computing 613
12.1 Introduction 613
12.2 AI with Graphics Processing Unit 614
12.3 AI with Tensor Processing Unit 618
12.4 AI with Intelligence Processing Unit 621
12.5 AI with Cloud Computing 622
12.5.1 Amazon AWS 623
12.5.2 Microsoft Azure 624
12.5.3 Google Cloud Platform 625
12.5.4 Comparison of AWS, Azure, and GCP 625
12.6 Web-Based AI 629
12.6.1 Django 629
12.6.2 Flask 629
12.6.3 Streamlit 634
12.6.4 Other Libraries 634
12.7 Packaging the Code 635
Pyinstaller 635
Nbconvert 635
Py2Exe 636
Py2app 636
Auto-Py-To-Exe 636
cx_Freeze 637
Cython 638
Kubernetes 639
Docker 642
PIP 647
12.8 AI with Edge Computing 647
12.8.1 Google Coral 647
12.8.2 TinyML 648
12.8.3 Raspberry Pi 649
12.9 Create a Mobile AI App 651
12.10 Quantum AI 653
12.11 Summary 657
12.12 Chapter Review Questions 657
Index 659
