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6.7960 Deep Learning

6.7960 - Deep Learning (Fall 2024, MIT OCW). Instructors: Prof. Phillip Isola, Prof. Sara Beery, and Dr. Jeremy Bernstein. This course covers the fundamentals of deep learning, including both theory and applications. Topics include neural net architectures (MLPs, CNNs, RNNs, graph nets, transformers), geometry and invariances in deep learning, backpropagation and automatic differentiation, learning theory and generalization in high dimensions, and applications to computer vision, natural language processing, and robotics. (from ocw.mit.edu)

Lecture 04 - Architectures: Grids

This lecture will focus mostly on convolutional neural networks, presenting them as a good choice when your data lies on a grid.

Go to the Course Home or watch other lectures:

Lecture 01 - Introduction to Deep Learning

Lecture 02 - How to Train a Neural Net

Lecture 03 - Approximation Theory

Lecture 04 - Architectures: Grids

Lecture 05 - Architectures: Graphs

Lecture 06 - Generalization Theory

Lecture 07 - Scaling Rules for Optimization

Lecture 08 - Architectures: Transformers

Lecture 09 - Hacker's Guide to Deep Learning

Lecture 10 - Architectures: Memory

Lecture 11 - Representation Learning: Reconstruction-Based

Lecture 12 - Representation Learning: Similarity-Based

Lecture 13 - Representation Learning: Theory

Lecture 14 - Generative Models: Basics

Lecture 15 - Generative Models: Representation Learning Meets Generative Modeling

Lecture 16 - Generative Models: Conditional Models

Lecture 17 - Generization: Out-of-Distribution (OOD)

Lecture 18 - Transfer Learning: Models

Lecture 19 - Transfer Learning: Data

Lecture 20 - Scaling Laws

Lecture 21 - Language Models

Lecture 22 - NULL

Lecture 23 - Metrized Deep Learning

Lecture 24 - Interference Models for Deep Learning

Lecture 25 - Pytorch Tutorial