Abstract: Welcome to deep learning building blocks. This is an intermediate tutorial on deep learning that focuses on how to design neural networks for various data types.
Because this is intermediate, we are not going to be introducing what a convolution is or backpropagation. We assume that you either already know about them, or more care about solving problems than understanding theory.
This tutorial progresses by introducing different types of data (often data that is hard for traditional ML to take advantage of). We then present neural network designs that typically work well with that type of data. If you have am exotic type of data that you don't see listed here, let me know and I'd be happy to cover it!
On a final note, because I feel that there are already pretty decent tutorials on working with image and text data out there, I'll start this series by talking about good old fashioned tabular data
The order in which these tutorials go is as follows:
2. Categorical Data
3. Variable Length Features
4. Ordered Variable Length Features
5. Real World Example
6. Installing What You'll Need
The first step to get running with these tutorials is to install virtualenv. Fortunately there is a great tutorial on hitchhiker's guide to python.
Please follow the steps in the guide. https://github.com/knathanieltucker/deep-learning-building-blocks
Bio: Nathaniel earned his AB/SM in Computer Science from Harvard. He previously worked as a Quant and Trader at Jane Street and Goldman Sachs before transitioning into the pure tech industry. Nathaniel worked as a Data Scientist at Facebook, a Product Manager at Microsoft and a Software Engineer at Google before joining Vicarious. He is an avid reader and learner. He teaches part time at General Assembly and is developing open source teaching material for data science, machine learning, and web development.
Lead Instructor, Data and Analytics | General Assembly
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