7: Deep Learning for Natural Language – Transformers

MIT 15.773 Hands-On Deep Learning Spring 2024 Instructor: Rama Ramakrishnan View the complete course: https://ocw.mit.edu/courses/15-773-ha... YouTube Playlist:    • MIT 15.773 Hands-On Deep Learning Spring 2024   Transformers are described via an airline travel-related example. License: Creative Commons BY-NC-SAMore information at https://ocw.mit.edu/termsMore courses at https://ocw.mit.eduSupport OCW at http://ow.ly/a1If50zVRlQ We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at https://ocw.mit.edu/comments.

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

6: Deep Learning for Natural Language – Embeddings
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6: Deep Learning for Natural Language – Embeddings

data-parallelism
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data-parallelism

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

Visualizing transformers and attention | Talk for TNG Big Tech Day '24
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Visualizing transformers and attention | Talk for TNG Big Tech Day '24

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

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

5: Deep Learning for Natural Language – The Basics
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5: Deep Learning for Natural Language – The Basics

Transformers, the tech behind LLMs | Deep Learning Chapter 5
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Transformers, the tech behind LLMs | Deep Learning Chapter 5

11: Generative AI – Text-to-Image Models
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11: Generative AI – Text-to-Image Models

Training Sand to Think: Artificial General Intelligence & Future of Physics
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Training Sand to Think: Artificial General Intelligence & Future of Physics

Creator of C++: Bell Labs, Negative Overhead Abstraction, Mistakes | Bjarne Stroustrup
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Creator of C++: Bell Labs, Negative Overhead Abstraction, Mistakes | Bjarne Stroustrup

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

The Story of Python and how it took over the world | Python: The Documentary
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The Story of Python and how it took over the world | Python: The Documentary

Nobel Prize lecture: Geoffrey Hinton, Nobel Prize in Physics
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Nobel Prize lecture: Geoffrey Hinton, Nobel Prize in Physics

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

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

MIT Introduction to Deep Learning | 6.S191
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MIT Introduction to Deep Learning | 6.S191

Ep. 1 - The History of Machine Learning with Tom Mitchell
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Ep. 1 - The History of Machine Learning with Tom Mitchell