Useful Resources
Books
- Michael Nielsen: Neural Networks and Deep Learning
- Goodfellow, Bengio and Courville: Deep Learning
- Simon Prince: Computer Vision models
- Computer Vision: Algorithms and Applications
Conferences
- CVPR: http://cs.stanford.edu/people/karpathy/cvpr2015papers/
- NIPS: http://papers.nips.cc/book/advances-in-neural-information-processing-systems-27-2014
Lectures
- http://cs231n.github.io/
- http://cs229.stanford.edu/materials.html
- http://www.seas.harvard.edu/courses/cs281/
MOOCs
- https://www.udacity.com/course/deep-learning--ud730 (offered by Google)
Blogs
- Christopher Olah’s blog contains many well-written and nicely illustrated explanations to basic deep learning topics.
- Andrej Kapathy
- Andrej Kapathy: A Hacker’s Guide to Neural Networks
- Ferenc Huszár: Rather strong focus on generative models involving sophisticated mathematics (mostly statistics).
- Distill: Journal-like website with interactive explanations.
- Explained Visually: Visual explanations of some machine learning topics.
- Lol’Log: Explains and compares various new techniques in deep learning.
- off the convex path: Blog focussing on theoretical ML
Software
- Keras https://keras.io
- Tensorflow: https://www.tensorflow.org/
- Caffe: http://caffe.berkeleyvision.org/
- PyTorch: http://pytorch.org/
- Theano: http://deeplearning.net/software/theano/ (deprecated)
