Tensorflow Learning Materials
Writings, tutorials, guides, and examples

Tensorflow Learning Materials

Some links on ML and Tensorflow readings, guides, and examples

[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.

Make Your Own Neural Network

Great (short) book by Tariq Rashid, guides the reader through writing a simple deep network from scratch in python.

Machine Learning for Artists

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.

MIT 6.S191: Introduction to Deep Learning

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.

Tensoflow Tutorials from GitHub user aymericdamien

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.

Tensorflow v2 Alpha Tutorials

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…

Deep Learning Book

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.

A Recipe for Training Neural Networks

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.