1. Introduction to Deep Learning

This chapter introduces the program structure, development environment(Python, Anaconda, Jupyter, and Pytorch), and real examples applying deep learning.

And before the starting main lectures, explains matrix operations and usage of NumPy.

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Official course description

The first part is an introduction to the program as well as a couple lessons covering tools you’ll be using. You’ll also get a chance to apply some deep learning models to do cool things like transferring the style of artwork to another image.

We’ll start off with a simple introduction to linear regression and machine learning. This will give you the vocabulary you need to understand recent advancements, and make clear where deep learning fits into the broader picture of machine learning techniques.

Jupyter Environment

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