1. Get yourself grounded in theory Learning from Data by Yaser Abu-Mostafa, and its companion book. Very theoretically heavy but worth the trouble (this is Cal

1. Get yourself grounded in theory Learning from Data by Yaser Abu-Mostafa, and its companion book. Very theoretically heavy but worth the trouble (this is Caltech course) Do all assignments in the course with Python/Numpy/Scikit-learn 2. Choose your niche You need to NARROW down your interest. You can start broad just to know enough basics, but gradually pinpoint to making/hacking stuff that is most fun for you. Test waters on computer vision, speech, NLP, and games/robotics (reinforcement learning), or other less popular fields. E.g. Start with computer vision -> Basic convnet image classification -> Encoder-Decoder architectures -> End-to-end ConvNet monocular RGB to depth image generator Read papers that have published code on github, this let you reproduce and understand how things work, so that after awhile you can hack your own models and stuff