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