Links

Some basic courses on ANNs, Backprop, etc.

https://www.youtube.com/watch?v=PNqc4fkdfIo  Jeff Heaton

https://www.youtube.com/watch?v=TjZBTDzGeGg&list=PLUl4u3cNGP63gFHB6xb-kVBiQHYe_4hSi

https://www.youtube.com/watch?v=bH6VnezBZfI

https://www.youtube.com/watch?v=WZDMNM36PsM

https://www.youtube.com/watch?v=0WtWtY7aIF8

https://www.youtube.com/watch?v=Ih5Mr93E-2c

https://www.youtube.com/watch?v=dz_jeuWx3j0

Deep Learning Nature Paper

NeoCognitron

part 1: https://www.youtube.com/watch?v=Qil4kmvm2Sw

part 2: https://www.youtube.com/watch?annotation_id=annotation_346506&feature=iv&src_vid=Qil4kmvm2Sw&v=oVYCjL54qoY

https://www.youtube.com/watch?v=e-HgzFkpd3U

https://www.youtube.com/watch?v=JnU6E_aAGXw

Essential Cheat Sheets for Machine Learning and Deep Learning Engineers

Neural Network Architectures

Creating Art with Conv Neural Nets

Cheat Sheet of Machine Learning and Python (and Math) Cheat Sheets

A casual intro to Machine Learning

Convolutional Attention Model for Natural Language Inference

Applying deep learning to real-world problems

Object detection with neural networks — a simple tutorial using keras

The Deep Learning Roadmap

A Peek at Trends in Machine Learning

ICML accepted papers institution stats

Tensor Flow for beginners

https://www.tensorflow.org/get_started/mnist/beginners

ICLR 2017 vs arxiv-sanity

Yes you should understand backprop

A new kind of deep neural networks

A nice set of talks by Cees Snoek

Conv Nets: A Modular Perspective

Neural Networks, Manifolds, and Topology

Attention and Augmented Recurrent Neural Networks

A curated list of deep learning resources for computer vision: http://jiwonkim.org/awesome-deep-vision/

UCLA Graduate Summer School: Deep Learning, Feature Learning, 2012

http://karpathy.github.io/

DL Tutorial 

http://www.kdnuggets.com/2016/03/must-know-tips-deep-learning-part-1.html/2

http://cs231n.github.io/neural-networks-2/

https://people.eecs.berkeley.edu/~junyanz/projects/gvm/index.html

https://www.engadget.com/2016/09/22/facebook-and-intel-reign-supreme-in-doom-ai-deathmatch/

Computational models of the visual cortex

Learning Python

https://www.tutorialspoint.com/python/python_command_line_arguments.htm

http://michaelnielsen.org/

ImageNet challenge!

http://image-net.org/challenges/LSVRC/2016/results

The Mostly Complete Chart of Neural Networks

UDACITY Course

Multiple view geometry by Daniel Cremers

A taxonomy of deep learning models

On information theory

A Tour of modern image processing by Peyman Milanfar

A Brief Overview of Deep Learning by Ilya Sutskever

Visualization 

http://www.creativeai.net/

Visualization

http://people.csail.mit.edu/torralba/research/drawCNN/drawNet.html?path=imagenetCNN

http://people.csail.mit.edu/torralba/research/drawCNN/drawNet.html

Visualizing CNNs link1 link2

An introduction to Generative Adversarial Networks (with code in TensorFlow)

Variational Autoencoders

DL Visualization

http://yosinski.com/deepvis

Style transfer

http://genekogan.com/works/style-transfer/

http://demos.algorithmia.com/deep-style/

https://github.com/jcjohnson/neural-style

deepart.io

Visualizing CNN internals

https://www.youtube.com/watch?v=GHVaaHESrlY

Deep CNNs are easily fooled

https://www.youtube.com/watch?v=M2IebCN9Ht4

CV code (old school CV!)

Generative Adversarial Networks (GANS)

https://github.com/Newmu/dcgan_code

https://arxiv.org/pdf/1606.03498v1.pdf

http://distill.pub/2016/deconv-checkerboard/

http://soumith.ch/eyescream/

https://openai.com/blog/generative-models/#contributions

https://github.com/openai/improved-gan

https://www.youtube.com/watch?v=HN9NRhm9waY&t=1646s

Deep Advances in Generative Modeling

https://www.youtube.com/watch?v=KeJINHjyzOU

GAN code

Adversarial images

http://www.evolvingai.org/fooling

http://image-net.org/challenges/talks/2016/Hikvision_at_ImageNet_2016.pdf

Conditional GANs

https://arxiv.org/abs/1411.1784

Generating faces with GAN in Torch

http://torch.ch/blog/2015/11/13/gan.html

Delving deep into GANs

https://hackernoon.com/how-do-gans-intuitively-work-2dda07f247a1#.sqk5bbasd

https://medium.com/@Moscow25/gans-will-change-the-world-7ed6ae8515ca#.fi850kqun

GAN example

GAN 1D Demo

Language Modeling

Word2Vec CBOW 1 & 2

Word2Vec SkipGram 1 & 2

Word2Vec and Glove (by Ali Ghodsi 1 & 2)

DL for NLP

Another explanation of Word2Vec and video

Recurrent Neural Networks

Understanding LSTM Networks

NIPS 2016 Symposium on RNNs

RNN part 1 and 2

RNN by Ali Ghodsi 1 & 2

RNN effectiveness

http://karpathy.github.io/2015/05/21/rnn-effectiveness/

MIT 6.S094: Deep Learning for Self-Driving Cars

Generating Sequences with RNNs

LSTMs for visual recognition 

Zero shot Learning

https://www.youtube.com/watch?v=jBnCcr-3bXc

Memory Networks

https://www.youtube.com/watch?v=ZwvWY9Yy76Q

https://www.youtube.com/watch?v=DjPQRLMMAbw&list=PLhr2SuUH5-CmzabE0AyEfaadE1uGejWkE&index=2

https://www.youtube.com/watch?v=BN7Kp0JD04o&index=3&list=PLhr2SuUH5-CmzabE0AyEfaadE1uGejWkE

https://www.youtube.com/watch?v=ZRYObdTOaEI&index=4&list=PLhr2SuUH5-CmzabE0AyEfaadE1uGejWkE

Stat841f11

DL in Action

Awesome Deep Learning

Deep Q learning

https://www.nervanasys.com/demystifying-deep-reinforcement-learning/

https://sites.google.com/site/describingmovies/

http://eccvw2016.wixsite.com/bioarteccv2016

http://web.stanford.edu/class/cs379c/archive/2010/handouts.html

http://norvig.com/ipython/README.html

http://norvig.com/mayzner.html

https://www.technologyreview.com/s/602246/what-robots-can-learn-from-babies/?set=602265

http://www.deeplearningbook.org/

https://www.eecs.qmul.ac.uk/~qian/Project_cvpr16.html

CVPR 2016 papers

http://colinlea.com/guides/2016/CVPR/index.html

1st Workshop on Action and Anticipation for Visual Learning

Deep Vision Workshop CVPR 2015

https://deepvision.forge.nicta.com.au/index.html#invited

Foundations of ML boot camp

https://simons.berkeley.edu/workshops/schedule/3748

DL for apps

http://ci2cv.net/16623/schedule/

Fukushima & Neocognitron

http://ac.els-cdn.com/S0893608011001018/1-s2.0-S0893608011001018-main.pdf?_tid=7a493e5e-bb42-11e6-961f-00000aacb362&acdnat=1480980667_b4bb39858627a07419426f14ea60fde3

First person vision (a talk by Ryo Yonetani)

Deep learning and CV courses

https://www.cs.princeton.edu/courses/archive/spring16/cos495/

http://www.cc.gatech.edu/~hays/7476/

http://web.cs.hacettepe.edu.tr/~erkut/bil722.f12/index.html

http://vision.princeton.edu/courses/COS598/2015sp/

Vision and language

https://www.cs.utexas.edu/~vsub/pdf/Translating_Videos_slides.pdf

David Rumelhardt

http://thesciencenetwork.org/programs/cogsci-2010/david-rumelhart

MIRC

http://www.pnas.org/content/113/10/2744.full.pdf?with-ds=yes

Convolutional Neural Fabrics https://arxiv.org/pdf/1606.02492.pdf

A talk by Yan LeCun

https://www.youtube.com/watch?v=3boKlkPBckA

Towards a Mathematical Theory of Cortical Micro-circuits    [Hierarchical Temporal Memory!]

George and Hawkins 

http://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1000532&type=printable

https://www.youtube.com/watch?v=Vy3CELyzKLA

Image saliency 

saliency.mit.edu

A nice talk by Rob Fergus

https://www.youtube.com/watch?v=2qvx1ED8ZO0

DL software links

Computer Vision Datasets link1 link2 link3 link4

Deep Drumpf!!

http://www.deepdrumpf2016.com/index.html

Demis Hassabis: Towards General Artificial Intelligence

Prof. Jürgen Schmidhuber - True Artificial Intelligence Will Change Everything

https://www.youtube.com/watch?v=XkltShNd6XE

http://people.idsia.ch/~juergen/

WaveNet:  Text to speech system (Deep Mind)

Computer Vision Taiwanese Group

https://www.facebook.com/groups/112719202116662/?hc_ref=NEWSFEED

Deep RL

https://www.youtube.com/watch?v=PtAIh9KSnjo&t=974s

Natural image statistics

http://www.cs.toronto.edu/~fleet/courses/cifarSchool09/slidesLyu.pdf

http://jupyter.org/ [iNoteBook]

Skip thought vectors

https://arxiv.org/pdf/1506.06726v1.pdf

Object Recognition with Convolutional Neural Networks in the Keras Deep Learning Library

Unsupervised learning of invariantrepresentations with low sample complexity:... [Tommy Poggio]

Towards Open Set Deep Networks by Abhijit Bendale, Terrance E. Boult

PsyPhy: A Psychophysics Driven Evaluation Framework for Visual Recognition

Hinton Talk 

https://www.youtube.com/watch?v=l2dVjADTEDU

DO DEEP CONVOLUTIONAL NETS REALLY NEED TO BE DEEP AND CONVOLUTIONAL?

https://arxiv.org/pdf/1603.05691.pdf

BLENDING LSTMS INTO CNNS

https://arxiv.org/pdf/1511.06433.pdf

http://cvisioncentral.com/vision-resources/

https://www.quora.com/What-are-the-best-resources-for-learning-Computer-Vision

6.S094: Deep Learning for Self-Driving Cars

DEEPLEARNING.UNIVERSITY !

http://memkite.com/deep-learning-bibliography/

What's wrong with convolutional neural networks?

https://www.quora.com/Whats-wrong-with-convolutional-neural-networks

A nice course by Laurenz Wiskott

https://www.ini.rub.de/PEOPLE/wiskott/Teaching/Material/index.html

Hardware stuff

NVIDIA resources

http://www.nvidia.com/object/machine-learning.html

Kaggle challenges

https://www.kaggle.com/

A course on convex optimization

http://www.seas.ucla.edu/~vandenbe/ee236b/

The 7 biggest problems facing science

http://www.vox.com/2016/7/14/12016710/science-challeges-research-funding-peer-review-process

A DARPA Perspective on Artificial Intelligence

Sparse Coding

The human eye

http://web.stanford.edu/class/cs379c/archive/2010/stanford.lecture.04.pdf

human vision videos

https://www.youtube.com/watch?v=OGqAM2Mykng

https://www.youtube.com/watch?v=keMF8YzQoRM

https://www.youtube.com/watch?v=BDJ8xyQjyhM

https://www.youtube.com/watch?v=rfdJyDfIHIc

https://www.youtube.com/watch?v=TbDFrbXiz2s

https://www.youtube.com/watch?v=o0DYP-u1rNM

https://www.youtube.com/watch?v=qrKZBh8BL_U

https://www.youtube.com/channel/UCX6b17PVsYBQ0ip5gyeme-Q

https://www.youtube.com/watch?v=fxGfZiVGL1U

1: https://www.youtube.com/watch?v=tBjOCxRM_RY

2: https://www.youtube.com/watch?v=bjL-Z7fW2FM

https://www.youtube.com/watch?v=gkrM1gMpqRU&list=PLtXCbh6IFA7QCsei-t8WesusKi8I2LXUJ

brain facts

http://www.brainfacts.org/Educators/Educator-Resources/Articles-Folder/Brain-Facts-Download-the-Audio-Recording

http://www.cns.nyu.edu/faculty.php#core

http://www.cns.nyu.edu/corefaculty/Hawken.php

http://www.cns.nyu.edu/labs/

http://pesaranlab.org/teaching/

http://www.cns.nyu.edu/undergrad/courses/2011-2012/spring/#NEURL-UA220

http://www.cns.nyu.edu/~lcv/research.php

Thomas serre

Talk 1: https://www.youtube.com/watch?v=r1pjoQm9j7M

Talk 2: https://www.youtube.com/watch?v=S3Ye1FGRdNA

Talk 3: https://www.youtube.com/watch?v=4v_-c0LeGrU

https://www.youtube.com/watch?v=FV2nHIgkZCo&t=167s

Brain as a universal computing machine 

Vision and the Eye

Misc

http://distill.pub/2016/augmented-rnns/

https://www.linkedin.com/pulse/what-i-learned-from-deep-learning-summer-school-2016-hamid-palangi

https://www.pinterest.com/explore/computer-vision/

http://yosinski.com/deepvis

https://research.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html

http://www.bzarg.com/p/how-a-kalman-filter-works-in-pictures/

http://www.wisdom.weizmann.ac.il/~dannyh/Mircs/mircs.html

https://arxiv.org/pdf/1311.4158.pdf

Machine Learning e-book

Comparing humans and machine

https://www.youtube.com/watch?v=u0jGvR6clv4

...

TensorFlow

Books

    D. Forsyth and J. Ponce, Computer Vision: A Modern Approach, Prentice Hall, 2002

    R. Szeliski, Computer Vision: Algorithms and Applications, Springer 2010

    R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, Cambridge University Press, 2004

    http://www.deeplearningbook.org/

    Neural Networks and Deep Learning

    Vision Science, S. Palmer, MIT Press.  (Palmer) (electronic book; QP475 .P24 1999b)

    Vision book by David Marr https://mitpress.mit.edu/books/vision-0

    Computer Vision: Algorithms and Applications - Richard Szeliski - The book is available for free online or available for purchase.

    The Visual Neurosciences, 2vol. set Edited by Leo M. Chalupa and John S. Werner Sensation and Perception, E. Bruce Goldstein

    Understanding Vision Theory, Models, and Data by Li Zhaoping http://ukcatalogue.oup.com/product/9780199564668.do

    Forsyth, David A., and Ponce, J. Computer Vision: A Modern Approach, Prentice Hall, 2003.   

    Computational Neuroscience of Vision https://www.amazon.com/Computational-Neuroscience-Vision-Edmund-Rolls/dp/0198524889

    Computational models of visual processing by Landy and Movshon, MIT Press link

    Natural Image Statistics, Aapo Hyvärinen, Jarmo Hurri, and Patrik O. Hoyer, Springer-Verlag, 2009. link

    Visual Perception: The Neurophysiological Foundations edited by Lothar Spillmann, John S. Werner link

    K. Murphy, Machine Learning: A Probabilistic Perspective, The MIT Press, 2012

    C. Bishop, Pattern Recognition and Machine Learning, Springer 2007

    T. Mitchell, Machine Learning, McGraw-Hill, 1997

Other CV courses 

    UCF computer vision courses: http://crcv.ucf.edu/courses/

    MIT Advances in Computer Vision at MIT: http://6.869.csail.mit.edu/fa15/

    UT Austin Visual Recognition: http://www.cs.utexas.edu/~cv-fall2012/schedule.html

    UC Berkeley Visual Object and Activity Recognition: https://sites.google.com/site/ucbcs29443/

    CMU computer vision courses: http://vasc.ri.cmu.edu/vision_courses/V_Course_Detail.htm

    Stanford Convolutional Neural Networks for Visual Recognition: http://cs231n.stanford.edu/

    https://www.cs.princeton.edu/courses/archive/spring16/cos495/

    http://www.cc.gatech.edu/~hays/7476/

    http://web.cs.hacettepe.edu.tr/~erkut/bil722.f12/index.html

    http://vision.princeton.edu/courses/COS598/2015sp/

    Udacity ML

    https://people.eecs.berkeley.edu/~efros/courses/LBMV07/

    http://archive.org/search.php?query=vs265%20berkeley 

    Learning Course by Tommy Poggio at MIT

    Caltech ML course

    Machine learning by Thrun

    https://www.youtube.com/watch?v=ICKBWIkfeJ8&list=PLAwxTw4SYaPkQXg8TkVdIvYv4HfLG7SiH

    ML Course by Ng

    https://www.youtube.com/watch?v=UzxYlbK2c7E

    DL Course

    http://user.ceng.metu.edu.tr/~emre/Fall2016_DeepLearning.html

    Computer Vision Resources by Jia-Bin Huang

    http://www.cvpapers.com/

    http://homepages.inf.ed.ac.uk/rbf/CVonline/

    http://deeplearning.cs.cmu.edu/

Programming resources

    MATLAB “Must-Know” Tips: http://www.mathworks.com/matlabcentral/fileexchange/5685-writing-fast-matlab-code

    OpenCV: http://opencv.org/

    Deep learning software links: http://deeplearning.net/software_links/

    Comparison of DL software

    Popular Deep Learning Tools – a review

Software and code for DL

    CNN Architecture, convolution, and pooling by Andrej Karpathy: http://cs231n.github.io/convolutional-networks/

    VGG CNN practical by Andrea Vedaldi and Andrew Zisserman: http://www.robots.ox.ac.uk/~vgg/practicals/cnn/

    Intro to ConvNets by Kashif Rasul: https://www.youtube.com/watch?v=W9_SNGymRwo

    A lecture on CNN by Nando de Freitas: https://www.youtube.com/watch?v=bEUX_56Lojc

Top computer vision conferences

    CVPR: IEEE Conference on Computer Vision and Pattern Recognition 

    ICCV: International Conference on Computer Vision 

    ECCV: European Conference on Computer Vision 

    NIPS: Neural Information Processing Systems

    ICLR: International Conference in Learning Representations

    BMVC: European Machine Vision Conference

    ACCV: Asian Conference in Computer Vision

Misc

Ten simple rules for structuring papers

http://biorxiv.org/content/biorxiv/early/2016/12/14/088278.full.pdf

The Ph.D. Grind

Bill Freeman's talks

In particular: The most important qualities for success in graduate school 

and also: How to do research?

How to read research papers by Mubarak Shah: http://crcv.ucf.edu/people/faculty/HowToRead.html

A curated list of deep learning resources for computer vision: http://jiwonkim.org/awesome-deep-vision/Advice for Research Students

UCLA IPAM Computer Vision Summer School 2013.

Notes on paper editing by Konrad Kording.

Some resources for neuroscience.

How to write a good grant proposal.

Ten simple rules for structuring papers

Ph.D Movie.

...