A hands-on roadmap to using Python for artificial intelligence programming 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