DataScienceAI Course List | 机器学习、深度学习与自然语言处理领域推荐的课程列表

AI | 人工智能

DataScience & Statistics

Machine Learning | 机器学习

Deep Learning

  • 2016-Deep Learning by Google #Course#: In this course, you’ll develop a clear understanding of the motivation for deep learning, and design intelligent systems that learn from complex and/or large-scale datasets.

  • 2017-CS 20SI: TensorFlow for Deep Learning Research #Course#: This course will cover the fundamentals and contemporary usage of the TensorFlow library for deep learning research. We aim to help students understand the graphical computational model of TensorFlow, explore the functions it has to offer, and learn how to build and structure models best suited for a deep learning project.

  • DeepLearning AI #Course#: Most of the library is quite well tested since many students have used it to complete the Practical Deep Learning for Coders course. However it hasn't been widely used yet outside of the course, so you may find some missing features or rough edges. Personal notes can be found here; 关联的课件、代码等资源可以查看这里

  • 2018-Deep Learning Specialization #Course#: Deep Learning is transforming multiple industries. This five-course specialization will help you understand Deep Learning fundamentals, apply them, and build a career in AI.

  • 2018-CS230: Deep Learning: In this course, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. 百度网盘,j2vp

Reinforcement Learning

  • 2018-Deep Reinforcement Learning Course: Deep Reinforcement Learning Course is a free series of blog posts and videos 🆕 about Deep Reinforcement Learning, where we'll learn the main algorithms, and how to implement them with Tensorflow.

NLP | 自然语言处理

Industrial Applications | 行业应用

Autonomous Driving | 自动驾驶

Examples | 示范


  • 2015-TensorFlow Examples: This tutorial was designed for easily diving into TensorFlow, through examples. For readability, it includes both notebooks and source codes with explanation.

  • 2016-Deep Learning Using TensorFlow: This repository contains the code for TensorFlow Tutorials for Deep Learning from Starting to End. All the code is written using Python3.

  • 2017-Deep Learning 21 Examples: 本工程是《21 个项目玩转深度学习———基于 TensorFlow 的实践详解》的配套代码,代码推荐的运行环境为:Ubuntu 14.04,Python 2.7、TensorFlow >= 1.4.0。请尽量使用类 UNIX 系统和 Python 2 运行本书的代码。

  • 2017-TensorFlow Models by Sarasra #Project#: This repository contains a number of different models implemented in TensorFlow: the official models, the research models, the samples folder and the tutorials folder.

  • Android TensorFlow Machine Learning Example #Project#: This article is for those who are already familiar with machine learning and know how to the build model for machine learning(for this example I will be using a pre-trained model).

  • 2017-NakedTensor #Project#: Bare bone examples of machine learning in TensorFlow.

  • 2018-Deep Learning Using TensorFlow #Project#: This repository contains the code for TensorFlow Tutorials for Deep Learning from Starting to End. All the code is written using Python3.

  • 2018-TensorFlow Project Template #Project#: A simple and well designed structure is essential for any Deep Learning project, so after a lot of practice and contributing in tensorflow projects here's a tensorflow project template that combines simplcity, best practice for folder structure and good OOP design.

  • 2018-Beginner TensorFlowjs Examples in Javascript #Project#: This is the easiest set of Machine Learning examples that I can find or make. I hope you enjoy it.

  • 2018-finch #Project#: Make NLP Flow in TensorFlow

  • 2019-TensorFlow in Practice #Course#: In this four-course Specialization, you’ll explore exciting opportunities for AI applications. Begin by developing an understanding of how to build and train neural networks.


  • Pytorch Examples: A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

  • PyTorch Tutorial #Project#: This repository provides tutorial code for deep learning researchers to learn PyTorch. In the tutorial, most of the models were implemented with less than 30 lines of code. Before starting this tutorial, it is recommended to finish Official Pytorch Tutorial.