[Updated July 11, 2019]
Beginning to absorb machine learning, and how I might apply it towards my interests, has been a daunting task these past few weeks. Even with all the hype, tensorflow has what feels like no (zero) good examples, tutorials, guides.
This is a list of the things I’ve used to understand machine learning concepts, tensorflow basics, and building a model.
[Note] these are ordered psuedo-chronologically, the idea being I wish I started at the first one, and ended with the last one.
Great (short) book by Tariq Rashid, guides the reader through writing a simple deep network from scratch in python.
Website and courses by Gene Kogan, and understanding neural networks from the non-engineering, creative perspective. The lecture are great to just sit back and watch, try to absorb before moving on to actually making anything.
This short course was taught January 2019 to MIT students, and is a great intro and overview of what machine learning and tensorflow can offer. There are just a few lectures, and some homework that goes with them.
These tutorials have the most stars on github, the I love them because they don’t have distracting dependencies. These tutorials also include both lower-level learning, plus later examples that use Keras layer.
The offical tutorials. These are good for getting a deeper explaination from the developers. However, the tutorials have a lot of distracting dependencies, and the explainations tend to say a lot yet say very little…
I read that this was considered the “bible” of machine learning. I found that while it can be fairly dense with mathematics, there are some invaluable bottom-line summaries, suggestions, and best-practices riddled throughout this book.
Coming from designing electro-mechanical and wireless systems, I know how the development, test, and debug approach is very import. This reading gives what seems like great advice for how to approach buidling a model, step-by-step, to help catch mistakes when they happen.