转：深度学习课程及深度学习公开课资源整理
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这里整理一批深度学习课程或者深度学习相关公开课的资源，持续更新，仅供参考。
1. Andrew Ng (吴恩达) 深度学习专项课程 by Coursera and deeplearning.ai
这是 Andrew Ng 老师离开百度后推出的第一个深度学习项目（deeplearning.ai)的一个课程: Deep Learning Specialization ，课程口号是：Master Deep Learning, and Break into AI. 作为 Coursera 联合创始人 和 机器学习网红课程 "Machine Learning" 的授课者，Andrew Ng 老师引领了数百万同学进入了机器学习领域，而这门深度学习课程的口号也透露了他的野心：继续带领百万人进入深度学习的圣地。
作为 Andrew Ng 老师的粉丝，依然推荐这门课程作为深度学习入门课程首选，并且建议花费上 Coursera 上的课程，一方面可以做题，另外还有证书，最重要的是它的编程作业，是理解课程内容的关键点，仅仅看视频绝对是达不到这个效果的。参考：《Andrew Ng 深度学习课程小记》和《Andrew Ng (吴恩达) 深度学习课程小结》。
2. Geoffrey Hinton 大神的 面向机器学习的神经网络（Neural Networks for Machine Learning）
Geoffrey Hinton大神的这门深度学习课程 2012年在 Coursera 上开过一轮，之后一直沉寂，直到 Coursera 新课程平台上线，这门课程已开过多轮次，来自课程图谱网友的评论：
"Deep learning必修课"
"宗派大师+开拓者直接讲课，秒杀一切二流子"
这门深度学习课程相对上面 Andrew Ng深度学习课程有一定难道，但是没有编程作业，只有Quiz.
3. 牛津大学深度学习课程(2015): Deep learning at Oxford 2015
这门深度学习课程名字虽然是 "Machine Learning 2014-2015"，不过主要聚焦在深度学习的内容上，可以作为一门很系统的机器学习深度学习课程：
Machine learning techniques enable us to automatically extract features from data so as to solve predictive tasks, such as speech recognition, object recognition, machine translation, question-answering, anomaly detection, medical diagnosis and prognosis, automatic algorithm configuration, personalisation, robot control, time series forecasting, and much more. Learning systems adapt so that they can solve new tasks, related to previously encountered tasks, more efficiently.
The course focuses on the exciting field of deep learning. By drawing inspiration from neuroscience and statistics, it introduces the basic background on neural networks, back propagation, Boltzmann machines, autoencoders, convolutional neural networks and recurrent neural networks. It illustrates how deep learning is impacting our understanding of intelligence and contributing to the practical design of intelligent machines.
视频Playlist：https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu
参考：“牛津大学Nando de Freitas主讲的机器学习课程，重点介绍深度学习，还请来Deepmind的Alex Graves和Karol Gregor客座报告，内容、讲解都属一流，强烈推荐！ 云: http://t.cn/RA2vSNX”
4. Udacity 深度学习（中/英）by Google
Udacity (优达学城）上由Google工程师主讲的免费深度学习课程，结合Google自己的深度学习工具 Tensorflow ，很不错：
机器学习是发展最快、最令人兴奋的领域之一，而深度学习则代表了机器学习中最前沿但也最有风险的一部分。在本课内容中，你将透彻理解深度学习的动机，并设计用于了解复杂和/或大量数据库的智能系统。
我们将教授你如何训练和优化基本神经网络、卷积神经网络和长短期记忆网络。你将通过项目和任务接触完整的机器学习系统 TensorFlow。你将学习解决一系列曾经以为非常具有挑战性的新问题，并在你用深度学习方法轻松解决这些问题的过程中更好地了解人工智能的复杂属性。
我们与 Google 的首席科学家兼 Google 智囊团技术经理 Vincent Vanhoucke 联合开发了本课内容。此课程提供中文版本。
5. Udacity 纳米基石学位项目：深度学习
Udacity的纳米基石学位项目，收费课程，不过据说更注重实战：
人工智能正颠覆式地改变着我们的世界，而背后推动这场进步的，正是深度学习技术。优达学城和硅谷技术明星一起，带来这门帮你系统性入门的课程。你将通过充满活力的硅谷课程内容、独家实战项目和专业代码审阅，快速掌握深度学习的基础知识和前沿应用。
你在实战项目中的每行代码都会获得专业审阅和反馈，还可以在同步学习小组中，接受学长、导师全程的辅导和督促
6. fast.ai 上的深度学习系列课程
fast.ai上提供了几门深度学习课程，课程标语很有意思：Making neural nets uncool again ，并且 Our courses (all are free and have no ads):
Deep Learning Part 1: Practical Deep Learning for Coders
Why we created the course
What we cover in the course
Deep Learning Part 2: Cutting Edge Deep Learning for Coders
Computational Linear Algebra: Online textbook and Videos
Providing a Good Education in Deep Learning—our teaching philosophy
A Unique Path to Deep Learning Expertise—our teaching approach
7. 台大李宏毅老师深度学习课程：Machine Learning and having it Deep and Structured
难得的免费中文深度学习课程：
课程主页：http://speech.ee.ntu.edu.tw/~tlkagk/courses_MLDS17.html
课程视频Playlist: https://www.youtube.com/playlist?list=PLJV_el3uVTsPMxPbjeX7PicgWbY7F8wW9
B站搬运深度学习课程视频: https://www.bilibili.com/video/av9770302/
8. 台大陈缊侬老师深度学习应用课程：Applied Deep Learning / Machine Learning and Having It Deep and Structured
据说是美女老师，这门课程16年秋季开过一次，不过没有视频，最新的这期是17年秋季课程，刚刚开课，Youtube上正在陆续放出课程视频：
16年课程主页，有Slides等相关资料：https://www.csie.ntu.edu.tw/~yvchen/f105-adl/index.html
17年课程主页，资料正在陆续放出：https://www.csie.ntu.edu.tw/~yvchen/f106-adl/
Youtube视频，目前没有playlist，可以关注其官方号放出的视频：https://www.youtube.com/channel/UCyB2RBqKbxDPGCs1PokeUiA/videos
9. Yann Lecun 深度学习公开课
"Yann Lecun 在 2016 年初于法兰西学院开课，这是其中关于深度学习的 8 堂课。当时是用法语授课，后来加入了英文字幕。
作为人工智能领域大牛和 Facebook AI 实验室（FAIR）的负责人，Yann Lecun 身处业内机器学习研究的最前沿。他曾经公开表示，现有的一些机器学习公开课内容已经有些过时。通过 Yann Lecun 的课程能了解到近几年深度学习研究的最新进展。该系列可作为探索深度学习的进阶课程。"
10. 2016 年蒙特利尔深度学习暑期班
推荐理由：看看嘉宾阵容吧，Yoshua Bengio 教授循环神经网络，Surya Ganguli 教授理论神经科学与深度学习理论，Sumit Chopra 教授 reasoning summit 和 attention，Jeff Dean 讲解 TensorFlow 大规模机器学习，Ruslan Salakhutdinov 讲解学习深度生成式模型，Ryan Olson 讲解深度学习的 GPU 编程，等等。
11. 斯坦福大学深度学习应用课程：CS231n: Convolutional Neural Networks for Visual Recognition
这门面向计算机视觉的深度学习课程由Fei-Fei Li教授掌舵，内容面向斯坦福大学学生，货真价实，评价颇高：
Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. During the 10-week course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. The final assignment will involve training a multi-million parameter convolutional neural network and applying it on the largest image classification dataset (ImageNet). We will focus on teaching how to set up the problem of image recognition, the learning algorithms (e.g. backpropagation), practical engineering tricks for training and fine-tuning the networks and guide the students through hands-on assignments and a final course project. Much of the background and materials of this course will be drawn from the ImageNet Challenge.
12. 斯坦福大学深度学习应用课程: Natural Language Processing with Deep Learning
这门课程由NLP领域的大牛 Chris Manning 和 Richard Socher 执掌，绝对是学习深度学习自然语言处理的不二法门。
Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. Applications of NLP are everywhere because people communicate most everything in language: web search, advertisement, emails, customer service, language translation, radiology reports, etc. There are a large variety of underlying tasks and machine learning models behind NLP applications. Recently, deep learning approaches have obtained very high performance across many different NLP tasks. These models can often be trained with a single end-to-end model and do not require traditional, task-specific feature engineering. In this winter quarter course students will learn to implement, train, debug, visualize and invent their own neural network models. The course provides a thorough introduction to cutting-edge research in deep learning applied to NLP. On the model side we will cover word vector representations, window-based neural networks, recurrent neural networks, long-short-term-memory models, recursive neural networks, convolutional neural networks as well as some recent models involving a memory component. Through lectures and programming assignments students will learn the necessary engineering tricks for making neural networks work on practical problems.
这门课程融合了两位授课者之前在斯坦福大学的授课课程，分别是自然语言处理课程 cs224n (Natural Language Processing)和面向自然语言处理的深度学习课程 cs224d (Deep Learning for Natural Language Processing).
13. 斯坦福大学深度学习课程: CS 20SI: Tensorflow for Deep Learning Research
准确的说，这门课程主要是针对深度学习工具Tensorflow的:
Tensorflow is a powerful open-source software library for machine learning developed by researchers at Google Brain. It has many pre-built functions to ease the task of building different neural networks. Tensorflow allows distribution of computation across different computers, as well as multiple CPUs and GPUs within a single machine. TensorFlow provides a Python API, as well as a less documented C++ API. For this course, we will be using Python.
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. Through the course, students will use Tensorflow to build models of different complexity, from simple linear/logistic regression to convolutional neural network and recurrent neural networks with LSTM to solve tasks such as word embeddings, translation, optical character recognition. Students will also learn best practices to structure a model and manage research experiments.
14. 牛津大学 & DeepMind 联合的面向NLP的深度学习应用课程: Deep Learning for Natural Language Processing: 2016-2017
课程主页：https://www.cs.ox.ac.uk/teaching/courses/2016-2017/dl/
github课程项目页面：https://github.com/oxford-cs-deepnlp-2017/
课程视频Playlist: https://www.youtube.com/playlist?list=PL613dYIGMXoZBtZhbyiBqb0QtgK6oJbpm
B站搬运视频: https://www.bilibili.com/video/av9817911/
15. 卡耐基梅隆大学（CMU）深度学习应用课程：CMU CS 11-747, Fall 2017 Neural Networks for NLP
课程主页：http://phontron.com/class/nn4nlp2017/
课程视频Playlist: https://www.youtube.com/watch?v=Sss2EA4hhBQ&list=PL8PYTP1V4I8ABXzdqtOpB_eqBlVAz_xPT
16. MIT组织的一个为期一周的深度学习课程: 6.S191: Introduction to Deep Learning http://introtodeeplearning.com/
17. 奈良先端科学技術大学院大学(NAIST) 2014年推出的一个深度学习短期课程（英文授课）：Deep Learning and Neural Networks
18. Deep Learning course: lecture slides and lab notebooks
欢迎大家推荐其他没有覆盖到的深度学习课程。
注：本文首发“课程图谱博客”：http://blog.coursegraph.com ，同步发布到这里，原文链接地址：http://blog.coursegraph.com/深度学习课程资源整理，转载请注明出处。
