Hey guys!

This is a collection of python notebooks with implementation of many machine learning algorithms (mainly neural nets).

I use 2 python packages for ML:

1. Keras
2. Tensorflow

I use keras for the high-level APIs to build the neural nets faster - designing the neural net architecture. I like this, but what is interesting in ML is to know whats happening under the hood.

Hence, I use low-level tensorflow APIs to understand the algorithms better by coding in the inner workings of algorithms (layer by layer).

This repo is made as a learning activity, and if you land up here, I hope the notebooks help you understand the "black box" of ML.

I am open to suggestions/code-edits/ideas for other notebooks!

Thanks!