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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.

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Your start-to-finish roadmap to AI programming with Python

Artificial intelligence remains one of the most exciting areas of computer science in modern history. It has applications in everything from people management to finance, and opens a world of possibilities for programmers, computer scientists, and other technology professionals.

In Artificial Intelligence Programming with Python^®: From Zero to Hero, distinguished photophysicist and veteran educator Dr. Perry Xiao shows you how to harness the power of the Python programming language to accelerate your introduction to AI coding.

The book demystifies the concepts of artificial intelligence and teaches you its fundamentals from scratch, using plain, simple language and illustrative code examples.

Divided into three easy-to-understand sections, the book explains general concepts in AI, machine learning, and deep learning. The author also tackles a variety of practical subjects, like classification, regression in machine learning, and generative adversarial networks.

Artificial Intelligence Programming with Python^® presents hands-on introductions to Python and other widely used software tools, as well as expansive discussions of supervised, semi-supervised, and unsupervised machine learning. You’ll also get quick reference “cheat sheet” guides for artificial intelligence programming in Python that you can use again and again.

A can’t-miss guide for anyone with a basic knowledge of programming—including familiarity with variables, arrays, loops, if-else statements, and file input and output—this book also offers:

• Thorough introductions to AI, machine learning, and deep learning
• Descriptions of Python
• Discussions of supervised and unsupervised learning
• Explorations of classification and regression in supervised learning
• Explanations of clustering, PCA, and LDA in unsupervised learning
• Fulsome treatments of deep learning, cloud computing, and edge computing

정보제공 :

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