(***) Machine Learning course at Coursera by Andrew Ng.
A required course for any people who are interested in machine learning.

(***) Blog posts by Adam Geitgey, his code examples (tensorflow + jupyter) are amazing (GitHub link).
Step by step introduction to machine learning using tensorflow.

(*) Track deep learning related papers on Arxiv

(**) nice blog posts from a stanford graduate Andrej Karpathy.
A few excellent posts to introduce deep learning from a software engineer perspective.

(*) learn important features from deep learning models

(**) Berkeley CS294-112 Deep Reinforcement Learning Sp17

(*) Deep Learning 2016: The Year in Review

(*) stanford deep learning in genomics and biomedicine

(*) google Udacity deep learning course

(*) Keras Documentation

(*) deep learning for sequence data DragoNN

(*) deep learning for chemical data: deepchem

(*) mofan python (chinese).
Only provide a Chinese version, but a nice intro with code available in github.

(*) add image and text in one model (here)

(*) GAN code

(*) Open AI generative model post

(*) introduction-generative-adversarial-networks-code-tensorflow with github code

(**)tips and tricks training GAN.
If you have trouble debugging GAN, read this.

(***) CycleGAN: Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more.. Very inspiring application of GAN in domain knowledge translation.

(***) Deep learning with cats.
Using different GAN methods to generate cats.

(*) install tensorflow https://www.tensorflow.org/install/install_mac#ValidateYourInstallation