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I recently did exactly this, and currently think of the process as several layers working in parallel.

(1) implementation skill building: tensorflow udacity MOOC, and tutorials on the TF website

(2) implementing projects: find a research paper you're interested in, and try implementing it. e.g. "A Neural Algorithm of Artistic Style"

(3) foundational ML learning: Bengio's textbook, Michael Nielsen's textbook, cs231n, the Udacity ML MOOCs which end with the course on Reinforcement Learning, ... this list could go on for quite some time, which can be anxious for autodidactics because teaching yourself a thing means that your knowledge will be quite lean, but that's OK.

(4) cutting-edge ML learning: join a deep learning reading group / meet-up, and read influential papers weekly

(5: optional) write a technical blog, where the audience is yourself before understanding something.

Also, having high-level conceptual maps when entering an unfamiliar space is useful. For this, I recommend reading all of colah.github.io and Bengio's paper "Representation Learning: A Review and New Perspectives"




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