This material is not intended to be a full course on machine, deep learning, or neural networks, and is meant to introduce basic Pytorch functionality based on a number of examples. Pre-requisites are:

• Basic Linear Algebra
• Experience with Python Programming and the scientific python stack (Numpy, Matplotlib, ...) is recommended.
• Some familiarity with Neural Networks, Optimization, Convolutional Neural Networks and their concepts.

All code is meant to be run on Google Colab and was built on Pytorch 1.0.

Session	Exercise (Colab)	Solutions (Colab)
Getting Started: Google Colab and Logistics	Exercise
Session 1: Pytorch, Automatic Differentiation, Neural Nets	Exercise	Solutions
Session 2: Training Deep Neural Networks	Exercise	Solutions
Session 3: Convolutional Neural Networks	Exercise	Solutions
Project: The Seismic-NIST Dataset	Dataset	Benchmark