Artificial Intelligence Programming with Python

PERRY XIAO, PhD, is Professor and Course Director of London South Bank University. He holds his doctorate in photophysics and is Director and co-Founder of Biox Systems Ltd., a university spin-out company that designs and manufactures the AquaFlux and Epsilon Permittivity Imaging system.

• Preface xxiiiPart I IntroductionChapter 1 Introduction to AI 31.1 What Is AI? 31.2 The History of AI 51.3 AI Hypes and AI Winters 91.4 The Types of AI 111.5 Edge AI and Cloud AI 121.6 Key Moments of AI 141.7 The State of AI 171.8 AI Resources 191.9 Summary 211.10 Chapter Review Questions 22Chapter 2 AI Development Tools 232.1 AI Hardware Tools 232.2 AI Software Tools 242.3 Introduction to Python 272.4 Python Development Environments 302.4 Getting Started with Python 342.5 AI Datasets 452.6 Python AI Frameworks 472.7 Summary 492.8 Chapter Review Questions 50Part II Machine Learning and Deep LearningChapter 3 Machine Learning 533.1 Introduction 533.2 Supervised Learning: Classifications 55Scikit-Learn Datasets 56Support Vector Machines 56Naive Bayes 67Linear Discriminant Analysis 69Principal Component Analysis 70Decision Tree 73Random Forest 76K-Nearest Neighbors 77Neural Networks 783.3 Supervised Learning: Regressions 803.4 Unsupervised Learning 89K-means Clustering 893.5 Semi-supervised Learning 913.6 Reinforcement Learning 93Q-Learning 953.7 Ensemble Learning 1023.8 AutoML 1063.9 PyCaret 1093.10 LazyPredict 1113.11 Summary 1153.12 Chapter Review Questions 116Chapter 4 Deep Learning 1174.1 Introduction 1174.2 Artificial Neural Networks 1204.3 Convolutional Neural Networks 1254.3.1 LeNet, AlexNet, GoogLeNet 1294.3.2 VGG, ResNet, DenseNet, MobileNet, EffecientNet, and YOLO 1404.3.3 U-Net 1524.3.4 AutoEncoder 1574.3.5 Siamese Neural Networks 1614.3.6 Capsule Networks 1634.3.7 CNN Layers Visualization 1654.4 Recurrent Neural Networks 1734.4.1 Vanilla RNNs 1754.4.2 Long-Short Term Memory 1764.4.3 Natural Language Processing and Python Natural Language Toolkit 1834.5 Transformers 1874.5.1 BERT and ALBERT 1874.5.2 GPT-3 1894.5.3 Switch Transformers 1904.6 Graph Neural Networks 1914.6.1 SuperGLUE 1924.7 Bayesian Neural Networks 1924.8 Meta Learning 1954.9 Summary 1974.10 Chapter Review Questions 197Part III AI ApplicationsChapter 5 Image Classification 2015.1 Introduction 2015.2 Classification with Pre-trained Models 2035.3 Classification with Custom Trained Models: Transfer Learning 2095.4 Cancer/Disease Detection 2275.4.1 Skin Cancer Image Classification 2275.4.2 Retinopathy Classification 2295.4.3 Chest X-Ray Classification 2305.4.5 Brain Tumor MRI Image Classification 2315.4.5 RSNA Intracranial Hemorrhage Detection 2315.5 Federated Learning for Image Classification 2325.6 Web-Based Image Classification 2335.6.1 Streamlit Image File Classification 2345.6.2 Streamlit Webcam Image Classification 2425.6.3 Streamlit from GitHub 2485.6.4 Streamlit Deployment 2495.7 Image Processing 2505.7.1 Image Stitching 2505.7.2 Image Inpainting 2535.7.3 Image Coloring 2555.7.4 Image Super Resolution 2565.7.5 Gabor Filter 2575.8 Summary 2625.9 Chapter Review Questions 263Chapter 6 Face Detection and Face Recognition 2656.1 Introduction 2656.2 Face Detection and Face Landmarks 2666.3 Face Recognition 2796.3.1 Face Recognition with Face_Recognition 2796.3.2 Face Recognition with OpenCV 2856.3.3 GUI-Based Face Recognition System 288Other GUI Development Libraries 3006.3.4 Google FaceNet 3016.4 Age, Gender, and Emotion Detection 3016.4.1 DeepFace 3026.4.2 TCS-HumAIn-2019 3056.5 Face Swap 3096.5.1 Face_Recognition and OpenCV 3106.5.2 Simple_Faceswap 3156.5.3 DeepFaceLab 3226.6 Face Detection Web Apps 3226.7 How to Defeat Face Recognition 3346.8 Summary 3356.9 Chapter Review Questions 336Chapter 7 Object Detections and Image Segmentations 3377.1 Introduction 337R-CNN Family 338YOLO 339SSD 3407.2 Object Detections with Pretrained Models 3417.2.1 Object Detection with OpenCV 3417.2.2 Object Detection with YOLO 3467.2.3 Object Detection with OpenCV and Deep Learning 3517.2.4 Object Detection with TensorFlow, ImageAI, Mask RNN, PixelLib, Gluon 354TensorFlow Object Detection 354ImageAI Object Detection 355MaskRCNN Object Detection 357Gluon Object Detection 3637.2.5 Object Detection with Colab OpenCV 3647.3 Object Detections with Custom Trained Models 3697.3.1 OpenCV 369Step 1 369Step 2 369Step 3 369Step 4 370Step 5 3717.3.2 YOLO 372Step 1 372Step 2 372Step 3 373Step 4 375Step 5 3757.3.3 TensorFlow, Gluon, and ImageAI 376TensorFlow 376Gluon 376ImageAI 3767.4 Object Tracking 3777.4.1 Object Size and Distance Detection 3777.4.2 Object Tracking with OpenCV 382Single Object Tracking with OpenCV 382Multiple Object Tracking with OpenCV 3847.4.2 Object Tracking with YOLOv4 and DeepSORT 3867.4.3 Object Tracking with Gluon 3897.5 Image Segmentation 3897.5.1 Image Semantic Segmentation and Image Instance Segmentation 390PexelLib 390Detectron2 394Gluon CV 3947.5.2 K-means Clustering Image Segmentation 3947.5.3 Watershed Image Segmentation 3967.6 Background Removal 4057.6.1 Background Removal with OpenCV 4057.6.2 Background Removal with PaddlePaddle 4237.6.3 Background Removal with PixelLib 4257.7 Depth Estimation 4267.7.1 Depth Estimation from a Single Image 4267.7.2 Depth Estimation from Stereo Images 4287.8 Augmented Reality 4307.9 Summary 4317.10 Chapter Review Questions 431Chapter 8 Pose Detection 4338.1 Introduction 4338.2 Hand Gesture Detection 4348.2.1 OpenCV 4348.2.2 TensorFlow.js 4528.3 Sign Language Detection 4538.4 Body Pose Detection 4548.4.1 OpenPose 4548.4.2 OpenCV 4558.4.3 Gluon 4558.4.4 PoseNet 4568.4.5 ML5JS 4578.4.6 MediaPipe 4598.5 Human Activity Recognition 461ActionAI 461Gluon Action Detection 461Accelerometer Data HAR 4618.6 Summary 4648.7 Chapter Review Questions 464Chapter 9 GAN and Neural-Style Transfer 4659.1 Introduction 4659.2 Generative Adversarial Network 4669.2.1 CycleGAN 4679.2.2 StyleGAN 4699.2.3 Pix2Pix 4749.2.4 PULSE 4759.2.5 Image Super-Resolution 4759.2.6 2D to 3D 4789.3 Neural-Style Transfer 4799.4 Adversarial Machine Learning 4849.5 Music Generation 4869.6 Summary 4899.7 Chapter Review Questions 489Chapter 10 Natural Language Processing 49110.1 Introduction 49110.1.1 Natural Language Toolkit 49210.1.2 spaCy 49310.1.3 Gensim 49310.1.4 TextBlob 49410.2 Text Summarization 49410.3 Text Sentiment Analysis 50810.4 Text/Poem Generation 51010.5.1 Text to Speech 51510.5.2 Speech to Text 51710.6 Machine Translation 52210.7 Optical Character Recognition 52310.8 QR Code 52410.9 PDF and DOCX Files 52710.10 Chatbots and Question Answering 53010.10.1 ChatterBot 53010.10.2 Transformers 53210.10.3 J.A.R.V.I.S. 53410.10.4 Chatbot Resources and Examples 54010.11 Summary 54110.12 Chapter Review Questions 542Chapter 11 Data Analysis 54311.1 Introduction 54311.2 Regression 54411.2.1 Linear Regression 54511.2.2 Support Vector Regression 54711.2.3 Partial Least Squares Regression 55411.3 Time-Series Analysis 56311.3.1 Stock Price Data 56311.3.2 Stock Price Prediction 565Streamlit Stock Price Web App 56911.3.4 Seasonal Trend Analysis 57311.3.5 Sound Analysis 57611.4 Predictive Maintenance Analysis 58011.5 Anomaly Detection and Fraud Detection 58411.5.1 Numenta Anomaly Detection 58411.5.2 Textile Defect Detection 58411.5.3 Healthcare Fraud Detection 58411.5.4 Santander Customer Transaction Prediction 58411.6 COVID-19 Data Visualization and Analysis 58511.7 KerasClassifier and KerasRegressor 58811.7.1 KerasClassifier 58911.7.2 KerasRegressor 59311.8 SQL and NoSQL Databases 59911.9 Immutable Database 60811.9.1 Immudb 60811.9.2 Amazon Quantum Ledger Database 60911.10 Summary 61011.11 Chapter Review Questions 610Chapter 12 Advanced AI Computing 61312.1 Introduction 61312.2 AI with Graphics Processing Unit 61412.3 AI with Tensor Processing Unit 61812.4 AI with Intelligence Processing Unit 62112.5 AI with Cloud Computing 62212.5.1 Amazon AWS 62312.5.2 Microsoft Azure 62412.5.3 Google Cloud Platform 62512.5.4 Comparison of AWS, Azure, and GCP 62512.6 Web-Based AI 62912.6.1 Django 62912.6.2 Flask 62912.6.3 Streamlit 63412.6.4 Other Libraries 63412.7 Packaging the Code 635Pyinstaller 635Nbconvert 635Py2Exe 636Py2app 636Auto-Py-To-Exe 636cx_Freeze 637Cython 638Kubernetes 639Docker 642PIP 64712.8 AI with Edge Computing 64712.8.1 Google Coral 64712.8.2 TinyML 64812.8.3 Raspberry Pi 64912.9 Create a Mobile AI App 65112.10 Quantum AI 65312.11 Summary 65712.12 Chapter Review Questions 657Index 659