Practical Deep Learning Sprint Series

This event will be a guided sprint where you’ll attempt to solve an assignment covering the fundamentals of Deep Learning and Computer Vision and completing a basic code implementation of these concepts. A basic knowledge of Python is required since the primary aim of this session is to understand theory via implementation. It would be helpful if you go through some of the resources shared below, however that’s not a hard requirement. We’ll be helping you out wherever you get stuck.

The end goal will be to merge your implementations into this repository inside the submissions folder. If you’re not familiar with git don’t worry, we’ll guide you through it, however it would be great if you have your systems already set up.

Steps

• Download and setup Git (Resources shared below). Most MacOS and Linux machines come preinstalled with Git.
• Fork this repo on Github and clone the forked repo on your local machine.
• Attempt the assignment uploaded in the Assignment folder for the respective sprint date on Google Colab.
• Download the .ipynb file from Colab and save it in the submissions folder after renaming it as <your-name-enrolment-no.>.ipynb.
• Commit the changes that you have made using Git. Push the changes to your forked repo.
• Create a Pull Request (PR) to the original repository and wait for it to be merged.

You can try to complete the above steps as much as you can on your own before the sprint itself, although don't worry if you get stuck, the aim of the sprint itself would be to help you get through the implementation by the end of it.

Resources for Sprint #1 (1^st November 2022)

Basic Python:

Resource	Type
Free Code Camp's Python Tutorial	YouTube Video
Telusko's Python Tutorial	YouTube Playlist
Kaggle's Python course*	Annotated Notebook

Git and Github Basics:

Resource	Type
Installing Git (Windows) + Creating a GitHub Account	YouTube Video
Setup Workspace and Basic tools*	Blog

Basic Deep Learning

Resource	Type
3Blue1Brown's Intro to Neural Nets	YouTube Playlist
Kaggle's Intro to Deep Learning course*	Annotated Notebook

In case you aren't well acquintated with the above topics, try to get done with the * marked resources atleast before the sprint.

Resources for Sprint #2 (TBD)

Basic CNNs:

Resource	Type
Toward Data Science's Intro to CNNs	Blog
Toward Data Science's CNN Basics	Blog
Backpropagation through CNNs	Blog