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Lecture 01: Introduction to Deep Learning (6.7960)

Sara Beery · 1:00:51

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Lecture 02: How to Train a Neural Net (6.7960)

Sara Beery · 1:19:34

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Lecture 03: Approximation Theory (6.7960)

Jeremy Bernstein · 1:22:42

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Lecture 1: SQL Optimization Background (15-799)

Andy Pavlo · 53:57

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Lecture 2: History of Query Optimizers ft. IBM System R (15-799)

Andy Pavlo · 1:10:40

Lecture 01: Introduction to Deep Learning (6.7960)

Sara BeeryMIT

Professor

1:00:51·2026·graduate level
Path step 1: Frames representation learning as hierarchical composition of parameterized differentiable transformations.

This lecture provides an overview of deep neural networks, detailing their fundamental concepts and essential building blocks. It sets the foundation for understanding deep learning models.

AI & MLDeep LearningNeural NetworksCourse IntroductionModel Architectures

MIT 6.7960 Deep Learning, Fall 2024

Instructor: Sara Beery

View the complete course: https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/

YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63URZnh5iqBzDTDYPUTQT-8

Next · Step 2

Lecture 02: How to Train a Neural Net (6.7960)

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