Machine Learning

I've been training neural networks since before it was cool (circa 2016), and have been fortunate to contribute to a number of incredible innovations in AI through my work at Ezra:

I sometimes get asked how one can go about becoming a machine learning engineer, so I've put together a list of my favorite resources. I recommend you go through them in the exact order below.

  1. Neural Networks: Zero to Hero by Andrej Karpathy. This series will teach you how to build a neural network from scratch, including forward feed, backprop (including calculating the gradients manually). It's very useful for understanding the internals of a neural network.
  2. Practical Deep Learning by Jeremy Howard. Once you understand how a neural network works, it's time to get some actual practice building them (without worrying about the internals). I have found it's most useful to go through this once you've done Andrej's course above, as you'll be able to follow along much better.
  3. Read Practical Deep Learning for Coders. Jeremy Howard wrote this book before creating the Fast.ai Course (above). I recommend you do the course first, then read through the book and / or use it for reference.
  4. Read Deep Learning with Python by Fran├žois Chollet. Even though the book uses examples written using the Keras library (I prefer PyTorch), it does a pretty good job drilling the key concepts into your brain.

Going through the stuff above will give you a very sound foundation for becoming a machine learning engineer. From there, the only way to learn is through practice. Find an area you're interested in (for me, it's healthcare imaging data), find a Kaggle project in that area, and try to break into the top 10 for that project's leaderboard (FYI it's going to be pretty hard).

Good luck, and feel free to email or DM me on Twitter (@emigal) if I can be helpful.