Deep learning Courses

instructors: Xiaogang Wang. The Chinese University of Hong Kong - Spring 2015
intro: Homework, Homework Solutions, Lecture Notes, General Resources, Tutorial Notes, CUDA/GPU programming tutorial
homepage: https://piazza.com/cuhk.edu.hk/spring2015/eleg5040/resources

Self-Study Courses for Deep Learning (NVIDIA Deep Learning Institute)

homepage: https://developer.nvidia.com/deep-learning-courses

Introduction to Deep Learning

homepage: https://beta.bigdatauniversity.com/courses/introduction-deep-learning/

Deep Learning Courses

blog: http://machinelearningmastery.com/deep-learning-courses/

Creative Applications of Deep Learning w/ Tensorflow

homepage: https://www.kadenze.com/courses/creative-applications-of-deep-learning-with-tensorflow-i/info
github(ourse materials/Homework materials): https://github.com/pkmital/CADL

Deep Learning School: September 24-25, 2016 Stanford, CA

homepage: http://www.bayareadlschool.org/
day 1: https://www.youtube.com/watch?v=9dXiAecyJrY
day 2: https://www.youtube.com/watch?v=eyovmAtoUx0
github: https://github.com/lamblin/bayareadlschool
reddit: https://amp.reddit.com/r/MachineLearning/comments/54shmi/great_new_introductory_talks_on_various_subfields/
mirror: https://pan.baidu.com/s/1gfBe2fL

CSC 2541 Fall 2016: Differentiable Inference and Generative Models

homepage: http://www.cs.toronto.edu/~duvenaud/courses/csc2541/index.html

CS 294-131: Special Topics in Deep Learning (Fall, 2016)

https://berkeley-deep-learning.github.io/cs294-dl-f16/

Fork of Lempitsky DL for HSE master students.

github: https://github.com/yandexdataschool/HSE_deeplearning

ELEG 5040: Advanced Topics in Signal Processing (Introduction to Deep Learning)

resources: https://piazza.com/cuhk.edu.hk/spring2015/eleg5040/resources

CS 20SI: Tensorflow for Deep Learning Research

homepage: http://web.stanford.edu/class/cs20si/
github: https://github.com/chiphuyen/stanford-tensorflow-tutorials

Deep Learning with TensorFlow

https://bigdatauniversity.com/courses/deep-learning-tensorflow/

Deep Learning course

github: https://github.com/ddtm/dl-course

CSE 599G1: Deep Learning System

homepage: http://dlsys.cs.washington.edu/
assignments: http://dlsys.cs.washington.edu/assignments

CSC 321 Winter 2017: Intro to Neural Networks and Machine Learning

http://www.cs.toronto.edu/~rgrosse/courses/csc321_2017/

Theories of Deep Learning (STATS 385)

homepage: https://stats385.github.io/
video: https://www.researchgate.net/project/Theories-of-Deep-Learning
mirror: https://www.bilibili.com/video/av16136625/

CS230: Deep Learning Spring 2018

https://web.stanford.edu/class/cs230/

With Video Lectures

Deep Learning: Taking machine learning to the next level (Udacity)

instructor: Vincent Vanhoucke (Google), Arpan Chakraborty
homepage: https://www.udacity.com/course/deep-learning–ud730
homepage: https://cn.udacity.com/course/deep-learning–ud730/
homepage: https://classroom.udacity.com/courses/ud730/lessons/6370362152/concepts/63798118150923
assignments: https://github.com/tdhopper/udacity-deep-learning
ipn: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/udacity/1_notmnist.ipynb
ipn: http://nbviewer.jupyter.org/github/tensorflow/tensorflow/blob/master/tensorflow/examples/udacity/1_notmnist.ipynb
assignments: https://github.com/Arn-O/udacity-deep-learning

Neural networks class - Université de Sherbrooke

instructor: Hugo Larochelle
youtube: https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH
video: http://pan.baidu.com/s/1bnwEe8R
course content: http://info.usherbrooke.ca/hlarochelle/neural_networks/content.html
google group: https://groups.google.com/forum/#!forum/neural-networks-online-course

Deep Learning: Theoretical Motivations

author: Yoshua Bengio
published: Sept. 13, 2015. (Deep Learning Summer School, Montreal 2015)
video: http://videolectures.net/deeplearning2015_bengio_theoretical_motivations/
blog: http://rinuboney.github.io/2015/10/18/theoretical-motivations-deep-learning.html

University of Waterloo: STAT 946 - Deep Learning

homepage: https://uwaterloo.ca/data-science/deep-learning
video+slides: http://pan.baidu.com/s/1sjTRgjN

Deep Learning (2016) - BME 595A, Eugenio Culurciello, Purdue University

course shedule: http://t.cn/RVYQa69?u=1402400261&m=4034720314226808&cu=2261580215&ru=1402400261&rm=4034708389597157
mirror: https://pan.baidu.com/s/1hsBJOpQ
video: https://www.youtube.com/playlist?list=PLNgy4gid0G9cbw5OjwG2jxvFqYDqkGnpJ
mirror: https://pan.baidu.com/s/1bpKb5Cj

UVA DEEP LEARNING COURSE

intro: MSc in Artificial Intelligence for the University of Amsterdam.
homepage: http://uvadlc.github.io/
assignments: https://github.com/uvadlc/uvadlc_practicals_2016

Practical Deep Learning For Coders, Part 1

intro: 10 hours a week for 7 weeks
homepage: http://course.fast.ai/
youtube: https://www.youtube.com/playlist?list=PLfYUBJiXbdtS2UQRzyrxmyVHoGW0gmLSM
mirror: https://pan.baidu.com/s/1eRLK742#list/path=%2F
github: https://github.com/fastai/courses
blog: http://www.kdnuggets.com/2016/12/deep-learning-coders-mooc-jeremy-howard.html

T81-558:Applications of Deep Neural Networks

intro: Washington University
course page: https://sites.wustl.edu/jeffheaton/t81-558/
youtube: https://www.youtube.com/playlist?list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN
github: https://github.com/jeffheaton/t81_558_deep_learning

CS294-129 Designing, Visualizing and Understanding Deep Neural Networks

MIT 6.S191: Introduction to Deep Learning

homepage: http://introtodeeplearning.com/index.html
schedule(Slides+Videos): http://introtodeeplearning.com/schedule.html
github: https://github.com/yala/introdeeplearning
youtube: https://www.youtube.com/playlist?list=PLkkuNyzb8LmxFutYuPA7B4oiMn6cjD6Rs
mirror: https://pan.baidu.com/s/1qXXDCoG#list/path=%2F

Edx: Deep Learning Explained

intro: Microsoft
course page: https://www.edx.org/course/deep-learning-explained-microsoft-dat236x

Computer Vision

Stanford CS231n: Convolutional Neural Networks for Visual Recognition (Spring 2017)

Stanford CS231n: Convolutional Neural Networks for Visual Recognition (Winter 2016)

homepage: http://cs231n.stanford.edu/
homepage: http://vision.stanford.edu/teaching/cs231n/index.html
syllabus: http://vision.stanford.edu/teaching/cs231n/syllabus.html
course notes: http://cs231n.github.io/
youtube: https://www.youtube.com/watch?v=NfnWJUyUJYU&feature=youtu.be
mirror: http://pan.baidu.com/s/1pKsTivp
mirror: http://pan.baidu.com/s/1c2wR8dy
assignment 1: http://cs231n.github.io/assignments2016/assignment1/
assignment 2: http://cs231n.github.io/assignments2016/assignment2/
assignment 3: http://cs231n.github.io/assignments2016/assignment3/

ITP-NYU - Spring 2016

Video lectures and course notes: http://ml4a.github.io/classes/itp-S16/

Deep Learning for Computer Vision Barcelona: Summer seminar UPC TelecomBCN (July 4-8, 2016)

intro: This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
homepage(slides+videos): http://imatge-upc.github.io/telecombcn-2016-dlcv/
homepage: https://imatge.upc.edu/web/teaching/deep-learning-computer-vision
youtube: https://www.youtube.com/user/imatgeupc/videos?shelf_id=0&sort=dd&view=0

DLCV - Deep Learning for Computer Vision

homepage: https://imatge.upc.edu/web/teaching/deep-learning-computer-vision

Advanced Computer Vision Cap6412

Natural Language Processing

CS224n: Natural Language Processing with Deep Learning

intro: This course is a merger of Stanford’s previous cs224n course and cs224d
homepage: http://web.stanford.edu/class/cs224n/

Course notes for CS224N Winter17

https://github.com/stanfordnlp/cs224n-winter17-notes

Stanford CS224d: Deep Learning for Natural Language Processing

homepage: http://cs224d.stanford.edu/
syllabus: http://cs224d.stanford.edu/syllabus.html
lecture notes: https://cs224d.stanford.edu/lecture_notes/

Code for Stanford CS224D: deep learning for natural language understanding

github: https://github.com/bogatyy/cs224d

CMU CS 11-747, Fall 2017: Neural Networks for NLP

intro: by Graham Neubig
course page: http://phontron.com/class/nn4nlp2017/
github: https://github.com/neubig/nn4nlp2017-code
video: https://www.bilibili.com/video/av14153689/

Deep Learning for NLP - Lecture October 2015

github: https://github.com/UKPLab/deeplearning4nlp-tutorial/tree/master/2015-10_Lecture

Harvard University: CS287: Natural Language Processing

http://cs287.fas.harvard.edu/

Deep Learning for Natural Language Processing: 2016-2017

intro: Oxford Deep NLP 2017 course
homepage: http://www.cs.ox.ac.uk/teaching/courses/2016-2017/dl/
github: https://github.com/oxford-cs-deepnlp-2017/lectures
youtube: https://www.youtube.com/playlist?list=PL613dYIGMXoZBtZhbyiBqb0QtgK6oJbpm
mirror: https://pan.baidu.com/s/1dFvGHUh#list/path=%2F
mirror: https://pan.baidu.com/s/1c2tcC96

GPU Programming

Course on CUDA Programming on NVIDIA GPUs, July 27–31, 2015

homepage: http://people.maths.ox.ac.uk/gilesm/cuda/

An Introduction to GPU Programming using Theano

youtube: https://www.youtube.com/watch?v=eVd2TqEkVp0
video: http://pan.baidu.com/s/1c1i6LtI#path=%252F

GPU Programming

homepage: http://courses.cms.caltech.edu/cs179/

Parallel Programming

Intro to Parallel Programming Using CUDA to Harness the Power of GPUs (Udacity)

https://www.udacity.com/course/intro-to-parallel-programming–cs344

Fundamentals of Accelerated Computing with CUDA C/C++

intro: Learn to use CUDA C/C++ tools and techniques to accelerate CPU-only applications to run on massively parallel GPUs.
homepage: https://courses.nvidia.com/courses/course-v1:DLI+C-AC-01+V1/about

Workshops

Deep Learning: Theory, Algorithms, and Applications

homepage: http://doc.ml.tu-berlin.de/dlworkshop2017/
video: https://www.youtube.com/playlist?list=PLJOzdkh8T5kqCNV_v1w2tapvtJDZYiohW
mirror: https://www.bilibili.com/video/av15565354/

Resources

Open Source Deep Learning Curriculum

http://www.deeplearningweekly.com/pages/open_source_deep_learning_curriculum

Deep learning Courses

Deep Learning