Nine Recommended Programming Books for Home Learning

1. Python Neural Network Programming (195 pages)

This beginner‑friendly book guides readers to build a functional neural network using Python without requiring advanced mathematics, covering basic calculus and enabling the development of networks that can recognize handwritten digits and compete with professional models.

2. Python Web Crawling (196 pages)

An updated practical guide for Python 3.6, providing complete source code, database and caching techniques, and advanced topics such as using PyQt, Selenium, handling JavaScript‑heavy sites, CAPTCHAs, mechanize automation, Scrapy class‑based crawlers, testing, remote crawling, and image processing.

3. PyTorch Deep Learning (193 pages)

The book explains modern deep‑learning architectures like ResNet, DenseNet, Inception, and Seq2Seq, introduces GPU computing, PyTorch training methods, and generative networks, enabling readers to develop deep‑learning applications with PyTorch.

4. Machine Learning Essentials (172 pages)

A compact, full‑color guide covering supervised and unsupervised learning, SVMs, neural networks, ensemble methods, gradient descent, clustering, dimensionality reduction, autoencoders, transfer learning, feature engineering, and hyper‑parameter tuning, supplemented by QR‑code linked online resources and Python code examples.

5. Zero‑Trust Network (196 pages)

The first Chinese book on zero‑trust networking, offering a comprehensive overview of trust management, network proxies, device/user/application/traffic trust, implementation strategies, and attacker perspectives, emphasizing strong authentication, encryption, segmentation, and operational agility.

6. Programming Pearls (2nd Revised Edition, 259 pages)

A classic computer‑science work by Jon Bentley that blends deep insights, practical techniques, and entertaining anecdotes, focusing on selecting and efficiently implementing algorithms.

7. Python Mathematics Programming (189 pages)

Targeted at high‑school students, this book uses Python to explore algebra, statistics, geometry, probability, calculus, and fractals, offering visualizations, symbolic math, and creative programming challenges such as estimating areas and exploring the Fibonacci‑golden ratio relationship.

8. Artificial Intelligence Algorithms – Volume 1 (164 pages)

An introductory AI algorithm textbook covering dimensionality reduction, distance metrics, K‑means clustering, error calculation, hill climbing, simulated annealing, Nelder‑Mead, linear regression, with colorful examples, numerical calculations, and downloadable multi‑language code on GitHub.

9. Vim 8 Text Processing (238 pages)

A comprehensive Vim tutorial covering text editing techniques, VimScript scripting, Neovim, Oni editor, integration with Python, Git, and regular expressions, aimed at programmers with basic OS and programming knowledge, primarily focusing on Linux environments.

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