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6.7960

Deep Learning

Prof. Phillip Isola, Prof. Sara Beery, Dr. Jeremy Bernstein | Fall 2024

Data Science, Analytics & Computer Technology AI Machine Learning Visualization Computer Science Engineering Artificial Intelligence Graphics and Visualization

前往原始課程

CC BY-NC-SA 4.0

課程簡介

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.

Course Information

Source	MIT 開放式課程
科系	Electrical Engineering and Computer Science
Language	English
影片數	24

課程影片 (24)

Lec 01. Introduction to Deep Learning

Lec 01. Introduction to Deep Learning

Lec 02. How to Train a Neural Net

Lec 02. How to Train a Neural Net

Lec 03. Approximation Theory

Lec 03. Approximation Theory

Lec 04. Architectures: Grids

Lec 04. Architectures: Grids

Lec 05. Architectures: Graphs

Lec 05. Architectures: Graphs

Lec 06. Generalization Theory

Lec 06. Generalization Theory

Lec 07. Scaling Rules for Optimization

Lec 07. Scaling Rules for Optimization

Lec 08. Architectures: Transformers

Lec 08. Architectures: Transformers

Lec 09. Hacker's Guide to Deep Learning

Lec 09. Hacker's Guide to Deep Learning

Lec 10. Architectures: Memory

Lec 10. Architectures: Memory

Lec 11. Representation Learning: Reconstruction-Based

Lec 11. Representation Learning: Reconstruction-Based

Lec 12. Representation Learning: Similarity-Based

Lec 12. Representation Learning: Similarity-Based

Lec 13. Representation Learning: Theory

Lec 13. Representation Learning: Theory

Lec 14. Generative Models: Basics

Lec 14. Generative Models: Basics

Lec 15. Generative Models: Representation Learning Meets Generative Modeling

Lec 15. Generative Models: Representation Learning Meets Generative Modeling

Lec 16. Generative Models: Conditional Models

Lec 16. Generative Models: Conditional Models

Lec 17. Generalization: Out-of-Distribution (OOD)

Lec 17. Generalization: Out-of-Distribution (OOD)

Lec 18. Transfer Learning: Models

Lec 18. Transfer Learning: Models

Lec 19. Transfer Learning: Data

Lec 19. Transfer Learning: Data

Lec 20. Scaling Laws

Lec 20. Scaling Laws

Lec 21. Language Models

Lec 21. Language Models

Lec 23. Metrized Deep Learning

Lec 23. Metrized Deep Learning

Lec 24. Inference Methods for Deep Learning

Lec 24. Inference Methods for Deep Learning

PyTorch Tutorial

PyTorch Tutorial