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A bunch of subtitle files for a wide range of shows. Not all are perfectly timed so feel free to correct them and upload changes.
12 Weeks, 24 Lessons, AI for All!
Ai Learning Roadmap based on lots of open course sources and book materials. 我总结的 AI 学习路径。
「Java学习+面试指南」一份涵盖大部分 Java 程序员所需要掌握的核心知识。准备 Java 面试,首选 JavaGuide!
《动手学深度学习》:面向中文读者、能运行、可讨论。中英文版被70多个国家的500多所大学用于教学。
😮 Core Interview Questions & Answers For Experienced Java(Backend) Developers | 互联网 Java 工程师进阶知识完全扫盲:涵盖高并发、分布式、高可用、微服务、海量数据处理等领域知识
Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
Code of my MOOC Course <Play with Machine Learning Algorithms>. Updated contents and practices are also included. 我在慕课网上的课程《Python3 入门机器学习》示例代码。课程的更多更新内容及辅助练习也将逐步添加进这个代码仓。
🚀 fullstack tutorial 2022,后台技术栈/架构师之路/全栈开发社区,春招/秋招/校招/面试
Codes of my MOOC Course <Play with Algorithms>, Both in C++ and Java language. Updated contents and practices are also included. 我在慕课网上的课程《算法与数据结构》示例代码,包括C++和Java版本。课程的更多更新内容及辅助练习也将逐步添加进这个代码仓。
My blogs and code for machine learning. http://cnblogs.com/pinard
Codes of my MOOC Course <Play Data Structures in Java>. Updated contents and practices are also included. 我在慕课网上的课程《Java语言玩转数据结构》示例代码。课程的更多更新内容及辅助练习也将逐步添加进这个代码仓。
存放JAVA开发的设计思想、算法:《剑指Offer》、《编程珠玑》、《深入理解Java虚拟机:JVM高级特性与最佳实践》、《重构-改善既有代码的设计 中文版》、《clean_code(中文完整版)》、《Java编程思想(第4版)》、《Java核心技术 卷I (第8版)》、《Quartz_Job+Scheduling_Framework》;一些大的上传不上来的文件在README
PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation (《深度学习框架PyTorch:入门与实战》)
《可解释的机器学习--黑盒模型可解释性理解指南》,该书为《Interpretable Machine Learning》中文版
刷算法全靠套路,认准 labuladong 就够了!English version supported! Crack LeetCode, not only how, but also why.
Natural Language Processing with Transformers 中译本,最权威Transformers教程
Streamlit — A faster way to build and share data apps.
深度学习500问,以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述,以帮助自己及有需要的读者。 全书分为18个章节,50余万字。由于水平有限,书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作,联系scutjy2015@163.com 版权所有,违权必究 Tan 2018.06
This project implements a method that converts a trained XGBoost classification tree into a single decision tree.
Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2023
XGBoost for label-imbalanced data: XGBoost with weighted and focal loss functions