Lec 01. Introduction to Deep Learning
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Lec 01. Introduction to Deep Learning
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Lec 02. How to Train a Neural Net
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Lec 02. How to Train a Neural Net
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1: Introduction to Neural Networks and Deep Learning; Training Deep NNs
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Lec 23. Metrized Deep Learning
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MIT 6.S191: Deep Generative Modeling
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Lec 09. Hacker's Guide to Deep Learning
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Lec 09. Hacker's Guide to Deep Learning
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3: Deep Learning for Computer Vision – Building Convolutional Neural Networks from Scratch
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1:14:35
Lec 08. Architectures: Transformers
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59:19
MIT 6.S191: Reinforcement Learning
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1:22:42
Lec 03. Approximation Theory
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29:00
PyTorch Tutorial
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1:21:14
Lec 05. Architectures: Graphs
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Lec 04. Architectures: Grids
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Lec 24. Inference Methods for Deep Learning
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57:34
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6: Deep Learning for Natural Language – Embeddings
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Lec 11. Representation Learning: Reconstruction-Based
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Lec 14. Generative Models: Basics
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1:17:23
Lec 21. Language Models
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1:20:31
Lec 06. Generalization Theory
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1:25:41
Lec 18. Transfer Learning: Models
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1:20:40
Lec 15. Generative Models: Representation Learning Meets G…
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38:23
Lec 20. Scaling Laws
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1:16:22
4: Deep Learning for Computer Vision – Transfer Learning and Fin…
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1:17:04
5: Deep Learning for Natural Language – The Basics
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2: Training Deep NNs (cont.); Introduction to Keras/Tensorflow; …
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Lec 12. Representation Learning: Similarity-Based
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Lec 17. Generalization: Out-of-Distribution (OOD)
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Lec 19. Transfer Learning: Data
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