Artificial Intelligence Programming with Python From Zero to Hero

by Xiao, Perry

• ISBN13: 9781119820864
• ISBN10: 1119820863
• Edition: 1st
• Format: Paperback
• Copyright: 2022-03-29
• Publisher: Wiley

Summary

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.

Author Biography

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.

Table of Contents

Part I - Introduction
Chapter 1: Introduction to AI, Machine Learning and Deep Learning
Chapter 2: Introduction to MATLAB and Python
Part II - Machine Learning
Chapter 3: Supervised Learning - Classifications
Chapter 4: Supervised Learning - Regressions 
Chapter 5: Unsupervised Learning - Clustering, PCA, and LDA 
Chapter 6: Semi-supervised Learning, Reinforecement Learning and Machine Learning Applications
Part III - Deep Learning
Chapter 7: Deep Learning  - CNN, AlexNet, and GoogLeNet
Chapter 8: Deep Learning - RNN and LSTM
Chapter 9: Deep Learning - GAN
Chapter 10: Deep Learning - GPI, TPU, Cloud Computing and Edge Computing 
Appendix A: MATLAB for AI Cheat Sheets
Appendix B: Python for AI Cheat Sheets