Artificial Intelligence Programming with Python

Artificial Intelligence Programming with Python

by Perry Xiao
Released March 2022
Publisher(s): Wiley
ISBN: 9781119820864

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Book description

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

  1. Cover
  2. Title Page
  3. Preface
    1. Why Buy This Book
    2. How This Book Is Organized
    3. Example Code
    4. Who This Book Is For
    5. What This Book Is Not For
    6. What You Need
  4. Part I: Introduction
    1. CHAPTER 1: Introduction to AI
      1. 1.1 What Is AI?
      2. 1.2 The History of AI
      3. 1.3 AI Hypes and AI Winters
      4. 1.4 The Types of AI
      5. 1.5 Edge AI and Cloud AI
      6. 1.6 Key Moments of AI
      7. 1.7 The State of AI
      8. 1.8 AI Resources
      9. 1.9 Summary
      10. 1.10 Chapter Review Questions
    2. CHAPTER 2: AI Development Tools
      1. 2.1 AI Hardware Tools
      2. 2.2 AI Software Tools
      3. 2.3 Introduction to Python
      4. 2.4 Python Development Environments
      5. 2.4 Getting Started with Python
      6. 2.5 AI Datasets
      7. 2.6 Python AI Frameworks
      8. 2.7 Summary
      9. 2.8 Chapter Review Questions
  5. Part II: Machine Learning and Deep Learning
    1. CHAPTER 3: Machine Learning
      1. 3.1 Introduction
      2. 3.2 Supervised Learning: Classifications
      3. 3.3 Supervised Learning: Regressions
      4. 3.4 Unsupervised Learning
      5. 3.5 Semi-supervised Learning
      6. 3.6 Reinforcement Learning
      7. 3.7 Ensemble Learning
      8. 3.8 AutoML
      9. 3.9 PyCaret
      10. 3.10 LazyPredict
      11. 3.11 Summary
      12. 3.12 Chapter Review Questions
    2. CHAPTER 4: Deep Learning
      1. 4.1 Introduction
      2. 4.2 Artificial Neural Networks
      3. 4.3 Convolutional Neural Networks
      4. 4.4 Recurrent Neural Networks
      5. 4.5 Transformers
      6. 4.6 Graph Neural Networks
      7. 4.7 Bayesian Neural Networks
      8. 4.8 Meta Learning
      9. 4.9 Summary
      10. 4.10 Chapter Review Questions
  6. Part III: AI Applications
    1. CHAPTER 5: Image Classification
      1. 5.1 Introduction
      2. 5.2 Classification with Pre-trained Models
      3. 5.3 Classification with Custom Trained Models: Transfer Learning
      4. 5.4 Cancer/Disease Detection
      5. 5.5 Federated Learning for Image Classification
      6. 5.6 Web-Based Image Classification
      7. 5.7 Image Processing
      8. 5.8 Summary
      9. 5.9 Chapter Review Questions
    2. CHAPTER 6: Face Detection and Face Recognition
      1. 6.1 Introduction
      2. 6.2 Face Detection and Face Landmarks
      3. 6.3 Face Recognition
      4. 6.4 Age, Gender, and Emotion Detection
      5. 6.5 Face Swap
      6. 6.6 Face Detection Web Apps
      7. 6.7 How to Defeat Face Recognition
      8. 6.8 Summary
      9. 6.9 Chapter Review Questions
    3. CHAPTER 7: Object Detections and Image Segmentations
      1. 7.1 Introduction
      2. 7.2 Object Detections with Pretrained Models
      3. 7.3 Object Detections with Custom Trained Models
      4. 7.4 Object Tracking
      5. 7.5 Image Segmentation
      6. 7.6 Background Removal
      7. 7.7 Depth Estimation
      8. 7.8 Augmented Reality
      9. 7.9 Summary
      10. 7.10 Chapter Review Questions
    4. CHAPTER 8: Pose Detection
      1. 8.1 Introduction
      2. 8.2 Hand Gesture Detection
      3. 8.3 Sign Language Detection
      4. 8.4 Body Pose Detection
      5. 8.5 Human Activity Recognition
      6. 8.6 Summary
      7. 8.7 Chapter Review Questions
    5. CHAPTER 9: GAN and Neural-Style Transfer
      1. 9.1 Introduction
      2. 9.2 Generative Adversarial Network
      3. 9.3 Neural-Style Transfer
      4. 9.4 Adversarial Machine Learning
      5. 9.5 Music Generation
      6. 9.6 Summary
      7. 9.7 Chapter Review Questions
    6. CHAPTER 10: Natural Language Processing
      1. 10.1 Introduction
      2. 10.2 Text Summarization
      3. 10.3 Text Sentiment Analysis
      4. 10.4 Text/Poem Generation
      5. 10.5 Text to Speech and Speech to Text
      6. 10.6 Machine Translation
      7. 10.7 Optical Character Recognition
      8. 10.8 QR Code
      9. 10.9 PDF and DOCX Files
      10. 10.10 Chatbots and Question Answering
      11. 10.11 Summary
      12. 10.12 Chapter Review Questions
    7. CHAPTER 11: Data Analysis
      1. 11.1 Introduction
      2. 11.2 Regression
      3. 11.3 Time-Series Analysis
      4. 11.4 Predictive Maintenance Analysis
      5. 11.5 Anomaly Detection and Fraud Detection
      6. 11.6 COVID-19 Data Visualization and Analysis
      7. 11.7 KerasClassifier and KerasRegressor
      8. 11.8 SQL and NoSQL Databases
      9. 11.9 Immutable Database
      10. 11.10 Summary
      11. 11.11 Chapter Review Questions
    8. CHAPTER 12: Advanced AI Computing
      1. 12.1 Introduction
      2. 12.2 AI with Graphics Processing Unit
      3. 12.3 AI with Tensor Processing Unit
      4. 12.4 AI with Intelligence Processing Unit
      5. 12.5 AI with Cloud Computing
      6. 12.6 Web-Based AI
      7. 12.7 Packaging the Code
      8. 12.8 AI with Edge Computing
      9. 12.9 Create a Mobile AI App
      10. 12.10 Quantum AI
      11. 12.11 Summary
      12. 12.12 Chapter Review Questions
  7. Index
  8. Copyright
  9. Dedication
  10. About the Author
  11. About the Technical Editors
  12. Acknowledgments
  13. End User License Agreement

Product information

  • Title: Artificial Intelligence Programming with Python
  • Author(s): Perry Xiao
  • Release date: March 2022
  • Publisher(s): Wiley
  • ISBN: 9781119820864