Here is a resource summary for learning computer science. All the materials are divided into three categories and arranged in alphabetical order.
Also, this is a To-Do list for me. I hope this could be a beginner's guide to some specific fields in or related to computer science, such as machine learning, computer vision, and SLAM, etc.
You may refer to a webpage version here Link1 or Link2.
Updating!
Last Update: Mar. 10, 2018.
- content {:toc}
- Algorithms, (@Princeton) or Algorithms, Part I / Part II, (@Coursera)
- VisuAlgo.Net: visualising data structures and algorithms through animation
- Algorithms, (@Khan Academy)
- Visual Perception for Autonomous Driving, (CSC2541@Toronto)
- Deep Learning for Self-Driving Cars, (6.S094@MIT/SUTD)
- Computer Vision, (16-720@CMU, Prof. Martial Hebert)
- Convolutional Neural Networks for Visual Recognition, [网易云课堂], (CS231n@Stanford, Prof. Fei-Fei Li)
- Pratical Data Science, (15-388/688@CMU)
- Deep Learning Specialization, (@Coursera, Prof. Andrew Ng)
- Convolutional Neural Networks for Visual Recognition, [网易云课堂], (CS231n@Stanford, Prof. Fei-Fei Li)
- Introduction to Deep Learning, (6.S191@MIT)
- Neural Networks for Machine Learning, (@Coursera, Prof. Geoffrey Hinton)
- Practical Deep Learning For Coders, Part 1, (@USF)
- Deep Reinforcement Learning, Spring 2017, (CS294@UC Berkeley)
- Neural Networks and Deep Learning, a free online book
- Introduction to Computer Systems, (15-213@CMU)
- Fundamentals of Computing, (@Coursera, @Rice)
- Intensive Introduction to Computer Science Open Learning Course, (CS50@Harvard)
- Introduction to Computer Science and Programming Using Python, [@edX], [6.0001@MIT], [@网易公开课]
- Complete Notes of Stanford's Machine Learning Course, (@HoleHouse)
- Machine Learning, (@Coursera, Prof. Andrew Ng)
- Machine Learning for Data Analysis, (@Coursera, @Wesleyan)
- Neural Networks for Machine Learning, (@Coursera, Prof. Geoffrey Hinton)
- Machine Learning (2017,Fall), (@NTU, Dr. Hung-yi Lee/李宏毅博士)
- 机器学习, (@Prof. Zhihua Zhou/周志华教授)
- 机器学习的发展历程及启示, (@Prof. Zhihua Zhang/张志华教授)
- 统计学习方法, (@Dr. Hang Li/李航博士)
- Tensorflow for Deep Learning Research, (CS 20SI@Standford)
- TensorFlow初学者指南：如何为机器学习项目创建合适的文件架构
- TensorFlow入门教程
- Beginner’s Resources to Learn Programming Languages, (Recommended by Adam)
- IntelliJ IDEA, (IDE @JetBrains)
- Learn Java, (@Codecademy)
- Learn JavaScript, (@Codecademy)
- JavaScript教程, (@廖雪峰的官方网站)
- ANACONDA, (With over 720 popular packages easily installed popular, like Jupyter Notebook, numpy, scikit-learn, scipy, matplotlib)
- PyCharm, (IDE @JetBrains)
- Python, (@Codecademy)
- Video: Official Guide to Python, (@JetBrains)
- Python教程, (@廖雪峰的官方网站)
- MATLAB SKY, (Easy for Beginners)
- GitHub：GitHub官方入门教学视频
- Git教程, (@廖雪峰的官方网站)
- Coursera
- IMOOC / 慕课网
- Khan Academy
- RUNOOB / 菜鸟教程
- Udacity
- W3Cschool
- 廖雪峰的官方网站, (Tutorials for Java / JavaScript / Python / Git)
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