Course: MIT 6.S191: Introduction to Deep Learning

Lecture video: https://www.youtube.com/watch?v=QcLlc9lj2hk&list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI&index=4&ab_channel=AlexanderAmini

Course website: IntroToDeepLearning.com

Lecturers: Alexander Amini and Ava Soleimany

Find all my notes for this course in the ML Course Notes repo.

Please note that this is a rough draft of the notes, so you might find mistakes. The visuals and equations are directly obtained from the original slides which you can find on the course website. All of the credit goes to the lecturers. I simply hope that the notes serve as accompanying study material. The lecturers use TensorFlow but I am converting all codes to PyTorch.

Introduction to Generative Modeling

This lecture covers techniques and methods that looks at data and generates brand new data instances based on learned patterns from a model.

You may not be able to tell right away, but the following faces have been generated by a generative model train on large dataset with faces.

In the past lecture, we learned about supervised learning mostly, which covers models that learn a function that maps data to labels.