A Deep Learning Course with PyTorch

Table of Contents

Download Free Deep Learning Resource Guide

Introduction

The purpose of this project is to provide a comprehensive and yet simple course in Deep Learning using the PyTorch framework.

Motivation

Deep Learning, as a tool for Artificial Intelligence, has become increasingly popular in the industry of data science due to the ability to teach computers to learn by example. It is the driving technology behind modern autonomous vehicles as well as natural language processing. A considerable amount of literature has been published on Deep Learning. The purpose of this project is to provide the most important aspects of Deep Learning by presenting a series of simple and yet comprehensive tutorials using the PyTorch framework.In this project you will learn:

• What is Deep Learning?
• What are the basics of the linear algebra that make Deep Learning possible?
• What are the basics Neural Networks and how do they work?
• What can I learn more about Neural Networks?

Deep Learning

Introduction to Deep Learning

Title	Document
What is Deep Learning?	Tutorial
Why Deep Learning?	Tutorial
Applications	Tutorial

Basics

Title	Document
Logistic Regression	Tutorial
Derivatives and the Chain Rule	Tutorial
Gradient Descent	Tutorial

Title	Code	Document
Simple Neural Networks	Code	Tutorial
Simple Logistic Regression	Code	Tutorial

Neural Networks

Title	Document
Overview	Tutorial
Computation	Tutorial
Activation Functions	Tutorial
Backpropagation	Tutorial

Title	Code	Document
Simple Neural Network Classifier	Code	Tutorial

More on Deep Neural Networks

Title	Document
Regularization	Tutorial
Dropout	Tutorial
Gradient Setbacks	Tutorial
Batch Normalization	Tutorial

Pull Request Process

Please consider the following criterions in order to help us in a better way:

1. The pull request is mainly expected to be a link suggestion.
2. Please make sure your suggested resources are not obsolete or broken.
3. Ensure any install or build dependencies are removed before the end of the layer when doing a build and creating a pull request.
4. Add comments with details of changes to the interface, this includes new environment variables, exposed ports, useful file locations and container parameters.
5. You may merge the Pull Request in once you have the sign-off of at least one other developer, or if you do not have permission to do that, you may request the owner to merge it for you if you believe all checks are passed.

Contact Us

We are looking forward to your kind feedback. Please help us to improve this open source project and make our work better. For contribution, please create a pull request and we will investigate it promptly. Once again, we appreciate your kind feedback and support.

Please feel free to contact any of the developers for any feedback, questions, and concerns.

Developers

Developers	Email
Sam Burton	samb7@vt.edu
Matt Robinson	rmatt21@vt.edu
Andrew Whelan	wandrew8@vt.edu
Harrison Ellis	harry16@vt.edu
Brendan Bolon	brendb98@vt.edu

Supervisor: Amirsina Torfi [GitHub, Personal Website, Linkedin ]

NOTE: This project has been developed as a capstone project offered by [CS 4624 Multimedia/ Hypertext course at Virginia Tech] and Supervised and supported by [Machine Learning Mindset].

*: equally contributed

Image Sources

1. https://www.clipart.email/clipart/cartoon-simple-clipart-brain-392244.html
2. https://towardsdatascience.com/machine-learning-fundamentals-ii-neural-networks-f1e7b2cb3eef
3. https://en.wikipedia.org/wiki/Linear_algebra
4. https://stockadobe.com/184422188

Citation

If you found this course useful, please kindly consider citing it as below:

@software{amirsina_torfi_2019_3585763,
  author       = {Amirsina Torfi and
                  Samuel Burton and
                  Matt Robinson and
                  Andrew Whelan and
                  Harrison Ellis and
                  Brendan Bolon},
  title        = {{machinelearningmindset/machine-learning-course: 
                   Machine Learning with Python}},
  month        = april,
  year         = 2020,
  publisher    = {},
  version      = {1.0},
  doi          = {10.5281/zenodo.3585763},
  url          = {}
}