Variable Length Features and Deep Learning

If you’re like me, you don’t really need to train self-driving car algorithms or make a cat-image-detectors. Instead, you're likely dealing with practical problems and normal looking data.

The focus of this series is to help the practitioner develop intuition about when and how to use Deep Learning (DL) models in normal situations with normal data, e.g. structured (i.e. something you can read into pandas) data. I will teach you the fundamentals—the building blocks of DL.
There are many courses that teach DL on computer vision, NLP, etc. This is not that. We are about teaching the practitioner how to transform normal machine learning (ML) models into DL models—and I have a lot of experience doing just that.

Some existing DL courses are either overly theoretical (not useful to practitioners), overly simplistic (belying the sophistication), or even overly practical (providing the practitioner with a false sense of security). DL is hard. Real data science is hard. We want to steer you away from the most common mistakes.

By starting with tabular data, we can introduce you to the DL toolbox in a more intuitive way. Note, this series is not about the underlying math for neural networks or the like.
This series is aimed most directly at intermediate level users.

Helpful links:

Link to Deep Learning Building Blocks Series:
   • Python Keras — Deep Learning Building...  

Link to GitHub repo including tabular data lesson:
https://github.com/knathanieltucker/d...