Lec 01. Introduction to Deep Learning
1:00:51
Lec 01. Introduction to Deep Learning
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1: Introduction to Neural Networks and Deep Learning; Training Deep NNs
57:05
1: Introduction to Neural Networks and Deep Learning; Training Deep NNs
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Lec 02. How to Train a Neural Net
1:19:34
Lec 02. How to Train a Neural Net
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Lec 09. Hacker's Guide to Deep Learning
1:15:51
Lec 09. Hacker's Guide to Deep Learning
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3: Deep Learning for Computer Vision – Building Convolutional Neural Networks from Scratch
1:17:13
3: Deep Learning for Computer Vision – Building Convolutional Neural Networks from Scratch
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8: Deep Learning for Natural Language – Transformers, Self-Supervised Learning
1:16:47
8: Deep Learning for Natural Language – Transformers, Self-Supervised Learning
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5: Deep Learning for Natural Language – The Basics
1:17:04
5: Deep Learning for Natural Language – The Basics
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1:07:50
Lec 23. Metrized Deep Learning
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1:22:42
Lec 03. Approximation Theory
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29:00
PyTorch Tutorial
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1:20:31
Lec 06. Generalization Theory
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1:21:18
Lec 14. Generative Models: Basics
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1:18:22
2: Training Deep NNs (cont.); Introduction to Keras/Tensorflow; Application to Tabular Data
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1:16:38
7: Deep Learning for Natural Language – Transformers
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1:17:51
6: Deep Learning for Natural Language – Embeddings
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1:21:14
Lec 05. Architectures: Graphs
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1:23:57
Lec 04. Architectures: Grids
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1:17:23
Lec 21. Language Models
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1:14:35
Lec 08. Architectures: Transformers
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1:25:41
Lec 18. Transfer Learning: Models
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1:23:13
Lec 24. Inference Methods for Deep Learning
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1:16:20
Lec 12. Representation Learning: Similarity-Based
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1:13:28
Lec 10. Architectures: Memory
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38:23
Lec 20. Scaling Laws
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1:09:26
MIT Introduction to Deep Learning (2025) | 6.S191
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1:15:38
11: Generative AI – Text-to-Image Models
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1:14:30
9: Generative AI – Large Language Models (LLMs) and Retrieval Augmented Generation (RAG)
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1:21:04
Lec 11. Representation Learning: Reconstruction-Based
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57:34
MIT 6.S191: Recurrent Neural Networks, Transformers, and Attention
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YouTubeAlexander Amini
56:16
MIT Introduction to Deep Learning | 6.S191
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