Deep Learning Resources

Preface?

What is it?

This is a list of resources curated from the Slack channel for Udacity's first phase of AI Track scholarship challenge
Why use it?

As the Slack channel will have multiple conversations going on. I thought best to consolidate all the resources into one repository for anyone to access any time without having to go through all the conversations.
How to contribute?

All are welcome to open a Pull Request or raise an issue, with the content you would like to share
- What kind of content?
  
  Text tutorials, video resources, youtube playlists, self-paced learning courses, ebooks... I hope you get the point.
  
  All and any form of content can be shared here.

Contributors

Self-Paced Tutorials

CS231n: Convolutional Neural Networks for Visual Recognition This course is a deep dive into details of the deep learning architectures. Students will learn to implement, train and debug their own neural networks. The final assignment will involve training a multi-million parameter convolutional neural network and applying it on the largest image classification dataset (ImageNet). Much of the background and materials of this course will be drawn from the ImageNet Challenge.
- Notes to accompany the Stanford CS class CS231n: Convolutional Neural Networks for Visual Recognition
- Python Numpy Tutorial
Coursera: Introduction to Deep Learning & Neural Networks with Keras This course will introduce you to the field of deep learning
Practical Deep Learning for Coders Step by step introduction to Deep learning stuff!
- Pre-Requisite: You’ve been coding for at least a year, and also that (if you haven’t used Python before) you’ll be putting in the extra time to learn whatever Python you need as you go. (For learning Python, we have a list of python learning resources available.)
Elements of AI The Elements of AI is a series of free online courses created by Reaktor and the University of Helsinki. The courses combine theory with practical exercises and can be completed at your own pace.
Coursera: ML with Python This course dives into the basics of machine learning
Youtube: Machine Learning Recipes with Josh Gordon (offered on Google Developers Youtube Channel)

Full Stack Python Build, Deploy and Operate Python Applications
Scipy Lecture Notes One document to learn numerics, science, and data with Python

Youtube: 3Blue1Brown by Grant Sanderson, is combination of math and entertainment. The goal is for explanations to be driven by animations and for difficult problems to be made simple with changes in perspective.

The Deep Learning textbook by Ian Goodfellow and Yoshua Bengio and Aaron Courville
Machine Learning Mastery! by Jason Brownlee
Python Data Science Handbook by Jake VanderPlas
Quantitative Economics with Python by by Thomas J. Sargent and John Stachurski. This website presents a set of lectures on quantitative economic modeling in Python

Google Colab Colaboratory is a free Jupyter notebook environment provided by Google. If your local workstation cannot take the workload, Google Colab is the platform to use.