Deep Learning Guide

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First Steps

Start with Machine Learning course on Coursera.

Then you should move on to CS231n- course by Stanford, the notes on Github are intuitive.There is also another course by Harvard on Deep Learning for Natural Language Processing - CS224d. Another path recently followed by deep learning enthusiasts is the Deep Learning specialization on Coursera. The specialization contains 5 courses with proper case studies. There is also a Udacity course developed with Google. You can select any of the above courses as per your schedule.

After getting confident enough which you should try reading research papers, and implement some of them. One good resource for a collection of the most influential research paper is Deep Learning Roadmap.

And try participating in Kaggle competitions.

Which tools

You should be comfortable to convert any research paper to code. The community generally uses PyTorch for most research purpose, but Keras is easier to begin with. Other options are:

How to get help?

List of Interesting Blogs and Communities

Deep Reinforcement Learning

Reinforcement Learning (RL) is a particular way of framing the learning problem, so that instead of being given labels about which option is correct in any given situation (as in supervised learning), you only have a single scalar "reward" signal which you are trying to maximise[1]. For example, to learn to play chess, if you knew the best move in every situation, you could train a supervised learning (SL) system with these ground-truth labels. If you only have an entire game followed by a win/loss binary signal, you could use reinforcement learning to learn to play in such a way that wins are maximised. Thus RL is more generally applicable than SL. For example, AlphaGo was trained to play the board game of Go in this way, beating the world champion in a spectacular televised match.

Deep Reinforcement Learning (Deep RL) combines reinforcement learning with deep learning, using deep neural networks to learn directly from raw inputs, without hand-engineered features or domain-specific heuristics. In 2013 DeepMind arguably kicked off the growth of the field by showing impressive results using Deep RL to play Atari video games.

See Also

  1. Technically we're usually maximising sum-of-rewards over a single agent-environment interaction, e.g. a single video game episode.