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Browse materialsMIT 15.773 · Spring 2024

Hands-On Deep Learning

Subjects: Artificial Intelligence · Computer Science · Management · Science · Technology

Art: C. Morrison (engraver); Dodd, Robert (artist and…, An Outward bound East Indiaman sailing from the Downs - RMG PY8445, 29 May 1788 · Public domain

Instructor: Prof. Rama Ramakrishnan
Term: Spring 2024
Level: Graduate
What's inside: 11 videos · 26 other files · 9 web pages
Materials open: Videos here · files on MIT OCW

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About this course

Source: MIT OpenCourseWare 15.773 Hands-On Deep Learning, Spring 2024, Prof. Rama Ramakrishnan — original course page on ocw.mit.edu ↗. License: CC BY-NC-SA 4.0. Study order: MIT's own listing order, section by section. Taken from MIT's published course files, downloaded from ocw.mit.edu on September 22, 2026. How RedLectern is built.

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Lecture Notes

15 files

Assignments

10 video transcripts

Other materials from this course

15.773 SP24 Lecture 1 TranscriptPDF Download

MIT OpenCourseWare, Prof. Rama Ramakrishnan, 15.773 Hands-On Deep Learning, Spring 2024.

License: CC BY-NC-SA 4.0 · Edition: Spring 2024 · Open the original course on OCW ↗

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Hands-On Deep Learning

Before you start

What's here, and what isn't

Not included here

Materials by section

Lecture Notes

Assignments

Other materials from this course

1: Introduction to Neural Networks and Deep Learning; Training Deep NNs

2: Training Deep NNs (cont.); Introduction to Keras/Tensorflow; Application to Tabular Data

3: Deep Learning for Computer Vision – Building Convolutional Neural Networks from Scratch

4: Deep Learning for Computer Vision – Transfer Learning and Fine-Tuning; Intro to HuggingFace

5: Deep Learning for Natural Language – The Basics

6: Deep Learning for Natural Language – Embeddings

7: Deep Learning for Natural Language – Transformers

8: Deep Learning for Natural Language – Transformers, Self-Supervised Learning

9: Generative AI – Large Language Models (LLMs) and Retrieval Augmented Generation (RAG)

10: Generative AI – Adapting LLMs with Parameter-Efficient Fine-Tuning

11: Generative AI – Text-to-Image Models