Artificial Intelligence Programming with Python from Zero to Hero

✍ Scribed by Perry Xiao

• Publisher: John WIley
• Year: 2022
• Tongue: English
• Leaves: 716
• Edition: 1
• Category: Library

✦ Synopsis

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

Cover
Title Page
Copyright Page
About the Author
About the Technical Editors
Acknowledgments
Contents at a Glance
Contents
Preface
Part I Introduction
Chapter 1 Introduction to AI
1.1 What Is AI?
1.2 The History of AI
1.3 AI Hypes and AI Winters
1.4 The Types of AI
1.5 Edge AI and Cloud AI
1.6 Key Moments of AI
1.7 The State of AI
1.8 AI Resources
1.9 Summary
1.10 Chapter Review Questions
Chapter 2 AI Development Tools
2.1 AI Hardware Tools
2.2 AI Software Tools
2.3 Introduction to Python
2.4 Python Development Environments
2.4 Getting Started with Python
2.5 AI Datasets
2.6 Python AI Frameworks
2.7 Summary
2.8 Chapter Review Questions
Part II Machine Learning and Deep Learning
Chapter 3 Machine Learning
3.1 Introduction
3.2 Supervised Learning: Classifications
Scikit-Learn Datasets
Support Vector Machines
Naive Bayes
Linear Discriminant Analysis
Principal Component Analysis
Decision Tree
Random Forest
K-Nearest Neighbors
Neural Networks
3.3 Supervised Learning: Regressions
3.4 Unsupervised Learning
K-means Clustering
3.5 Semi-supervised Learning
3.6 Reinforcement Learning
Q-Learning
3.7 Ensemble Learning
3.8 AutoML
3.9 PyCaret
3.10 LazyPredict
3.11 Summary
3.12 Chapter Review Questions
Chapter 4 Deep Learning
4.1 Introduction
4.2 Artificial Neural Networks
4.3 Convolutional Neural Networks
4.3.1 LeNet, AlexNet, GoogLeNet
4.3.2 VGG, ResNet, DenseNet, MobileNet, EffecientNet, and YOLO
4.3.3 U-Net
4.3.4 AutoEncoder
4.3.5 Siamese Neural Networks
4.3.6 Capsule Networks
4.3.7 CNN Layers Visualization
4.4 Recurrent Neural Networks
4.4.1 Vanilla RNNs
4.4.2 Long-Short Term Memory
4.4.3 Natural Language Processing and Python Natural Language Toolkit
4.5 Transformers
4.5.1 BERT and ALBERT
4.5.2 GPT-3
4.5.3 Switch Transformers
4.6 Graph Neural Networks
4.6.1 SuperGLUE
4.7 Bayesian Neural Networks
4.8 Meta Learning
4.9 Summary
4.10 Chapter Review Questions
Part III AI Applications
Chapter 5 Image Classification
5.1 Introduction
5.2 Classification with Pre-trained Models
5.3 Classification with Custom Trained Models: Transfer Learning
5.4 Cancer/Disease Detection
5.4.1 Skin Cancer Image Classification
5.4.2 Retinopathy Classification
5.4.3 Chest X-Ray Classification
5.4.5 Brain Tumor MRI Image Classification
5.4.5 RSNA Intracranial Hemorrhage Detection
5.5 Federated Learning for Image Classification
5.6 Web-Based Image Classification
5.6.1 Streamlit Image File Classification
5.6.2 Streamlit Webcam Image Classification
5.6.3 Streamlit from GitHub
5.6.4 Streamlit Deployment
5.7 Image Processing
5.7.1 Image Stitching
5.7.2 Image Inpainting
5.7.3 Image Coloring
5.7.4 Image Super Resolution
5.7.5 Gabor Filter
5.8 Summary
5.9 Chapter Review Questions
Chapter 6 Face Detection and Face Recognition
6.1 Introduction
6.2 Face Detection and Face Landmarks
6.3 Face Recognition
6.3.1 Face Recognition with Face_Recognition
6.3.2 Face Recognition with OpenCV
6.3.3 GUI-Based Face Recognition System
Other GUI Development Libraries
6.3.4 Google FaceNet
6.4 Age, Gender, and Emotion Detection
6.4.1 DeepFace
6.4.2 TCS-HumAIn-2019
6.5 Face Swap
6.5.1 Face_Recognition and OpenCV
6.5.2 Simple_Faceswap
6.5.3 DeepFaceLab
6.6 Face Detection Web Apps
6.7 How to Defeat Face Recognition
6.8 Summary
6.9 Chapter Review Questions
Chapter 7 Object Detections and Image Segmentations
Chapter Outline
7.1 Introduction
R-CNN Family
YOLO
SSD
7.2 Object Detections with Pretrained Models
7.2.1 Object Detection with OpenCV
7.2.2 Object Detection with YOLO
7.2.3 Object Detection with OpenCV and Deep Learning
7.2.4 Object Detection with TensorFlow, ImageAI, Mask RNN, PixelLib, Gluon
TensorFlow Object Detection
ImageAI Object Detection
MaskRCNN Object Detection
Gluon Object Detection
7.2.5 Object Detection with Colab OpenCV
7.3 Object Detections with Custom Trained Models
7.3.1 OpenCV
Step 1
Step 2
Step 3
Step 4
Step 5
7.3.2 YOLO
Step 1
Step 2
Step 3
Step 4
Step 5
7.3.3 TensorFlow, Gluon, and ImageAI
TensorFlow
Gluon
ImageAI
7.4 Object Tracking
7.4.1 Object Size and Distance Detection
7.4.2 Object Tracking with OpenCV
Single Object Tracking with OpenCV
Multiple Object Tracking with OpenCV
7.4.2 Object Tracking with YOLOv4 and DeepSORT
7.4.3 Object Tracking with Gluon
7.5 Image Segmentation
7.5.1 Image Semantic Segmentation and Image Instance Segmentation
PexelLib
Detectron2
Gluon CV
7.5.2 K-means Clustering Image Segmentation
7.5.3 Watershed Image Segmentation
7.6 Background Removal
7.6.1 Background Removal with OpenCV
7.6.2 Background Removal with PaddlePaddle
7.6.3 Background Removal with PixelLib
7.7 Depth Estimation
7.7.1 Depth Estimation from a Single Image
7.7.2 Depth Estimation from Stereo Images
7.8 Augmented Reality
7.9 Summary
7.10 Chapter Review Questions
Chapter 8 Pose Detection
8.1 Introduction
8.2 Hand Gesture Detection
8.2.1 OpenCV
8.2.2 TensorFlow.js
8.3 Sign Language Detection
8.4 Body Pose Detection
8.4.1 OpenPose
8.4.2 OpenCV
8.4.3 Gluon
8.4.4 PoseNet
8.4.5 ML5JS
8.4.6 MediaPipe
8.5 Human Activity Recognition
ActionAI
Gluon Action Detection
Accelerometer Data HAR
8.6 Summary
8.7 Chapter Review Questions
Chapter 9 GAN and Neural-Style Transfer
9.1 Introduction
9.2 Generative Adversarial Network
9.2.1 CycleGAN
9.2.2 StyleGAN
9.2.3 Pix2Pix
9.2.4 PULSE
9.2.5 Image Super-Resolution
9.2.6 2D to 3D
9.3 Neural-Style Transfer
9.4 Adversarial Machine Learning
9.5 Music Generation
9.6 Summary
9.7 Chapter Review Questions
Chapter 10 Natural Language Processing
10.1 Introduction
10.1.1 Natural Language Toolkit
10.1.2 spaCy
10.1.3 Gensim
10.1.4 TextBlob
10.2 Text Summarization
10.3 Text Sentiment Analysis
10.4 Text/Poem Generation
10.5.1 Text to Speech
10.5.2 Speech to Text
10.6 Machine Translation
10.7 Optical Character Recognition
10.8 QR Code
10.9 PDF and DOCX Files
10.10 Chatbots and Question Answering
10.10.1 ChatterBot
10.10.2 Transformers
10.10.3 J.A.R.V.I.S.
10.10.4 Chatbot Resources and Examples
10.11 Summary
10.12 Chapter Review Questions
Chapter 11 Data Analysis
11.1 Introduction
11.2 Regression
11.2.1 Linear Regression
11.2.2 Support Vector Regression
11.2.3 Partial Least Squares Regression
11.3 Time-Series Analysis
11.3.1 Stock Price Data
11.3.2 Stock Price Prediction
Streamlit Stock Price Web App
11.3.4 Seasonal Trend Analysis
11.3.5 Sound Analysis
11.4 Predictive Maintenance Analysis
11.5 Anomaly Detection and Fraud Detection
11.5.1 Numenta Anomaly Detection
11.5.2 Textile Defect Detection
11.5.3 Healthcare Fraud Detection
11.5.4 Santander Customer Transaction Prediction
11.6 COVID-19 Data Visualization and Analysis
11.7 KerasClassifier and KerasRegressor
11.7.1 KerasClassifier
11.7.2 KerasRegressor
11.8 SQL and NoSQL Databases
11.9 Immutable Database
11.9.1 Immudb
11.9.2 Amazon Quantum Ledger Database
11.10 Summary
11.11 Chapter Review Questions
Chapter 12 Advanced AI Computing
12.1 Introduction
12.2 AI with Graphics Processing Unit
12.3 AI with Tensor Processing Unit
12.4 AI with Intelligence Processing Unit
12.5 AI with Cloud Computing
12.5.1 Amazon AWS
12.5.2 Microsoft Azure
12.5.3 Google Cloud Platform
12.5.4 Comparison of AWS, Azure, and GCP
12.6 Web-Based AI
12.6.1 Django
12.6.2 Flask
12.6.3 Streamlit
12.6.4 Other Libraries
12.7 Packaging the Code
Pyinstaller
Nbconvert
Py2Exe
Py2app
Auto-Py-To-Exe
cx_Freeze
Cython
Kubernetes
Docker
PIP
12.8 AI with Edge Computing
12.8.1 Google Coral
12.8.2 TinyML
12.8.3 Raspberry Pi
12.9 Create a Mobile AI App
12.10 Quantum AI
12.11 Summary
12.12 Chapter Review Questions
Index
EULA