Lecture Date and Time: December 5, 2020,
English: 9 PM IST/8.30 AM PST | Add to calendar (Google)
Hindi: 11 AM IST | Add to calendar (Google)
Notebooks:
- https://jovian.ai/aakashns/04-feedforward-nn
- https://jovian.ai/aakashns/fashion-feedforward-minimal
- https://jovian.ai/aakashns/dataviz-cheatsheet
Additional Resources
- A visual proof that neural networks can compute any function, also known as the Universal Approximation Theorem.
- But what is a neural network? - A visual and intuitive introduction to what neural networks are and what the intermediate layers represent
- Stanford CS229 Lecture notes on Backpropagation - for a more mathematical treatment of how gradients are calculated and weights are updated for neural networks with multiple layers.
What to do after the lecture?
- Run the Jupyter notebooks shared above (try other datasets)
- Ask and answer questions on this topic (scroll down)
- Start working on Assignment 3 - Feed Forward Neural Networks
Asking/Answering Questions :
Reply on this thread to ask questions during and after the lecture. Before asking, scroll through the thread and check if your question (or a similar one) is already present. If yes, just like it. During the lecture, we’ll answer 8-10 questions with the most likes. The rest will be answered on the forum. If you see a question you know the answer to, please post your answer as a reply to that question. Let’s help each other learn!