Lesson 5: Deep Learning 2018

You will learn about collaborative filtering through the example of making movie recommendations, and talk about key developments that occurred during the Netflix prize.

We will dig into some lower level details of deep learning: what happens inside the training loop, how optimizers like momentum and Adam work, and regularization using weight decay. You will learn how to think spatially about math concepts like the ‘chain rule’, ‘jacobian’, and ‘hessian’.

[prabhatism]

Thank you. Amazing explanation of Momentum and Adam optimization.

[samario_torres]

are there any restrictions to the amount of independent variables that you should run on a feed forward nn?

[kevalan]

Link to the "Machine Learning class"?

[JayeshSaita]

1:48:25 I laughed more than I should 😂😂😂 btw, great lesson as always 🤘