Machine Learning deep learning natural Language processing learning

Original address: http://www.cnblogs.com/cyruszhu/p/5496913.html

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1 Basics

L Andrew NG's machine learning video.

Connection: homepage, material.

L 2.2008-year Andrew Ng CS229 machine Learning

Of course, the basic method does not change much, so the courseware PDF downloadable is the advantage.

Chinese subtitles video @ NetEase Open class, English video @youtube, courseware [email protected]

L 3.Tom Mitchell Machine learning Video

His "machine learning" has been selected as a textbook in many courses, with Chinese version.

2 Advanced

L 3. Lin Huntian (HT Lin) Teacher's two courses.

Machine learning Cornerstone (Machines learning Foundations):

Mooc,all handout slides, free YouTube videos

Machine learning techniques (machines learning techniques):

Mooc,all Handout slides,free YouTube videos

L 4.2013 years Yaser Abu-mostafa (Caltech) learning from Data

Content more suitable for advanced, course video, courseware [email protected]

Yaser Abu-mostafa is a teacher of Lin Huntian (HT Lin) and the course content of Lin is similar to this class.

L 5. 2012 Kaiyu (Baidu) Zhang Yi (Rutgers) machine learning public class

Content more suitable for advanced, course homepage @ Baidu Library, courseware [email protected] Dragon Star Program

L prml/Introduction to machine learning/matrix analysis (computational)/neural Network and machine learning

3 Direction 3.1 Deep neural network

L General Understanding:

A Deep Learning Tutorial:from perceptrons to Algorithms

Introduction to deep learning algorithms

Deep learning from the bottom up

Yann LeCun, Yoshua Bengio & Geoffrey hinton,deep learning[j],nature.

L Ufldl:deep Learning Tutorial from Stanford, Chinese version.

Stanford the official Tutorial,andrew ng of the computer department. To understand the principle of DL, this is the best use.

L Deep Learning,ian Goodfellow,yoshua Bengio,aaron Courville. Currently the most authoritative DL textbook.

L Neural Networks for machine learning.

Geoffrey Hinton,department of Computer science, Hinton is one of the inventors of the inverse propagation algorithm and the contrast divergence algorithm, and is also an active catalyst for deep learning. There are videos and materials .

L Oxford Deep Learning

Nando de Freitas has a full set of videos in the deep learning course offered in Oxford.

L Wulide, Professor, Fudan University. Youku Video: "Deep learning course", speaking of a very master style.

• Other references:

L Neural Networks Class,hugo Larochelle from Universitéde Sherbrooke

L Deep Learning Course, CILVR Lab @ NYU

3.2 Machine Vision

L Fei-fei Li:CS231n:Convolutional Neural Networks for Visual recognition.

http://cs231n.stanford.edu/

3.3 Natural Language Processing

L Richard Socher:CS224d:Deep Learning for Natural Language processing

Http://cs224d.stanford.edu/syllabus.html,video.

L Dan Jurafsky and Christopher Manning, link to the NLP course on Coursera. Natural language Processing.

L Michael Collins, Columbia University, Natural Language processing, Coursera course.

L High quality video of the Naacl tutorial version is up Here:video

The home page for the course. ACL + naacl Tutorial:deep Learning for NLP (without Magic), link.

L Statistical Learning methods, Hangyuan li. Very famous, good at Natural language processing, the book is also in accordance with natural language processing to write.

3.4 Miscellaneous Goods

Guo Xiaoxian Links: https://www.zhihu.com/question/26006703/answer/63572833 Copyright belongs to the author. Commercial reprint please contact the author for authorization, non-commercial reprint please specify the source.

Also recommended to see the great God Yoshua Bengio recommendation (the link to the left is the paper, the right is the code), there is a theory of application (mainly used in CV and NLP) Page on Toronto, Home Page of Geoffrey Hinton Page on Toronto, Home Page of Ruslan R Salakhutdinov Page on Wustl, ynd/cae.py GitHub Page on ICML, Https://github.com/lisa-lab/pyle ... Page on JMLR, pylearn2) On the difficulty of training recurrent neural networks, Trainingrnns ImageNet classification with deep convolutional neural Networks, cuda-convnet-high-performance C++/cuda implementation O F convolutional neural Networks-google Project Hosting Linguistic regularities in continuous Space Word representations, Word2vec-tool for computing continuous distributed rep Resentations of words. -Google Project Hosting

Professional doctrine Links: https://www.zhihu.com/question/26006703/answer/90969591 Source: Know

"Deep Learning for Natural Language processing and related applications" Introduction: This document from Microsoft Research, the essence of a lot. If you need to fully understand, a certain machine learning base is required. But there are places that can make people Mauseton and open their eyes. Understanding Convolutions Introduction: This is an introduction to the image convolution operation of the article, said the more detailed "Deep learning and shallow learning" Introduction: Compared to the deep learning and shallow learning Good article, is a graduate of Zhejiang University, MIT read Bo's Chiyuan Zhang blog. "Java Machine learning" Description: Java machine learning-related platforms and open-source machine learning libraries, sorted by big data, NLP, computer vision, and deep learning classification. Looks pretty full, Java enthusiasts are worth collecting. "Machine Learning classic paper/survey collection" Introduction: Look at the topic you already know what is content, yes. There are a lot of classic machine learning papers that deserve careful and repetitive reading. The classic Book of machine learning Introduction: Summed up the classic machine learning books, including mathematical basis and algorithmic theory of books, can be used as a reference list. "Deep Learning 101" Introduction: Because in the last two years, deep learning has been hyped up in the media world (like Big Data). In fact, many people do not know what is deep learning. This article is from the very next. Tell you what deep learning really is! "Underactuated Robotics" Description: MIT's underactuated Robotics on October 1, 2014, which belongs to the MIT postgraduate class, and friends interested in robotics and nonlinear dynamical systems may wish to challenge this course!

Xiao Kai Links: https://www.zhihu.com/question/31785984/answer/72180444 Source: Know Copyright belongs to the author. Commercial reprint please contact the author for authorization, non-commercial reprint please specify the source.

Video and Lectures How to Create a mind by Ray Kurzweil-is a inspiring talk Deep learning, self-taught learning and unsupervised Feature learning by Andrew Ng Recent developments in deep learning by Geoff Hinton The unreasonable effectiveness of deep learning by Yann LeCun Deep learning of representations by Yoshua Bengio Principles of hierarchical temporal Memory by Jeff Hawkins Machine learning Discussion Group-deep Learning W/stanford AI Labs by Adam Coates Making sense of the world with deep learning by Adam Coates Demystifying unsupervised Feature learning by Adam Coates Visual Perception with deep learning by Yann LeCun

Category: Machine learning-Basic

Machine Learning deep learning natural Language processing learning