posted Jun 12, 2015, 9:09 AM by Chris G
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updated Jan 11, 2016, 11:05 AM
]
A Python great tutorial: How to implement a neural network
Using neural nets to recognize handwritten digits
How the backpropagation algorithm works
Braininspired algorithms may make for optimized computational networks
Recurrent Neural Networks Tutorial, Part 1 – Introduction to RNNs
NVIDIA Get Started with Deep Learning
 If you are a data scientist designing neural networks for image classification use the NVIDIA Deep Learning GPU Training System (DIGITS)
 If you are a deep learning researcher or developer choose one of these widelyused open source deep learning frameworks and accelerate it with the CUDA Deep Neural Network (cuDNN) library:
 Caffe – developed by Yangqing Jia while in the PhD program at University of California at Berkeley
 Theano  A Python library that allows you to efficiently define, optimize, and evaluate mathematical expressions involving multidimensional arrays
 Torch  A scientific computing framework with wide support for machine learning algorithms
 Andrew Ng's Coursera course provides a good introduction to deep learning (Coursera, YouTube)
 Yann LeCun’s NYU Course on Deep Learning, Spring 2014 (TechTalks)
 Geoffrey Hinton's “Neural Networks for Machine Learning” course from Oct 2012 (Coursera)
 Rob Fergus's "Deep Learning for Computer Vision" tutorial from NIPS 2013 (slides, video)
 Caltech's introductory deep learning course taught by Yasser AbuMostafa (YouTube)
 Stanford CS224d: Deep Learning for Natural Language Processing (video, slides, tutorials)

