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.
I plan to continue updating this list as I keep moving through materials, and learning more.
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…
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.