其他好书推荐

本版块列举一下编者推荐、但未被列入其他版块的书籍：

Introductory Books

• Yale N. Patt - 计算机系统概论

• Behrouz Forouzan - 计算机科学导论

• Charles Petzold - 编码：隐匿在计算机软硬件背后的语言

• 吴军 - 数学之美

C++ Programming

• Bjarne Stroustrup - C++程序设计原理与实践

• Anthony Williams - C++并发编程实战

• 侯捷 - STL源码剖析

Python Programming

• Luciano Ramalho - 流畅的Python

• Brett Slatkin - Effective Python

• 崔庆才 - Python 3网络爬虫开发实战

Go Programming

• 任洪彩 - Go专家编程

Concurrency & Parallel & Distributed Systems

• Kirill Bobrov - Grokking Concurrency
• 周志明 - 凤凰架构：构建可靠的大型分布式系统

Compilers & PL

• Thorsten Ball - 用Go语言自制解释器
• Thorsten Ball - 用Go语言自制编译器

Design & Development

• Brain Kernighan - 程序设计实践
• Robert C. Martin - 敏捷软件开发
• Erich Gamma - 设计模式：可复用面向对象软件的基础
• Elisabeth Freeman - Head First 设计模式
• Chris Richardson - 微服务架构设计模式
• John Ousterhout - A Philosophy of Software Design
• Martin Fowler - 重构：改善既有代码的设计

TCS

• Tom Stuart - 计算的本质
• Cristopher Moore - The Nature of Computation
• Donald Knuth - Concrete Mathematics: A Foundation for Computer Science
• David MacKay - Information Theory, Inference and Learning Algorithms

Systems & Architecture

• David A. Patterson - Computer Organization and Design RISC-V Edition
• Urs Hoelzle - The Datacenter as a Computer

Big Data & Databases

• 张俊林 - 大数据日知录
• Matt Housley - Fundamentals of Data Engineering
• Michael Stonebraker - Readings in Database Systems
• Marko Lukša - Kubernetes in Action
• Tyler Akidau - Streaming Systems
• 张磊 - 深入剖析Kubernetes
• 黄健宏 - Redis设计与实现
• 朱忠华 - 深入理解Kafka：核心设计与实践原理

Reinforcement Learning

• Mykel Kochenderfer - Algorithms for Decision Making
• 王树森 - 深度强化学习

Statistics & Causal Inference & Optimization

• Hernán MA - Causal Inference: What If
• Judea Pearl - The Book of Why
• Judea Pearl - 统计因果推理入门
• Judea Pearl - Causality: Models, Reasoning and Inference
• Stephen Morgan - Counterfactuals and Causal Inference
• Trevor Hastie - Computer Age Statistical Inference
• Peter Westfall - Understanding Advanced Statistical Methods
• Larry Wasserman - All of Nonparametric Statistics
• Richard McElreath - Statistical Rethinking: A Bayesian Course with Examples in R and Stan
• 温斯顿 - 运筹学：应用范例与解法
• Yurii Nesterov - Introductory Lectures on Convex Optimization
• Dimitris Bertsimas - Introduction to Linear Optimization

Career Development

• 俞甲子 - 程序员的自我修养
• David Thomas - 程序员修炼之道
• Camille Fournier - 技术为径
• 吴军 - 浪潮之巅

Trading & Finance

• Stefan Jansen - Machine Learning for Algorithmic Trading
• Tikhon Jelvis - Foundations of Reinforcement Learning with Applications in Finance
• Xinfeng Zhou - A Practical Guide To Quantitative Finance Interviews
• Mark Joshi - Quant Job Interview Questions And Answers
• Yves Hilpisch - Python金融衍生品大数据分析
• Sebastien Donadio - Developing High-Frequency Trading Systems
• Timothy Crack - Heard on the Street: Quantitative Questions from Wall Street Job Interviews
• Quant Wiki 中文量化百科