Lec 01. Introduction to Deep Learning
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Lec 01. Introduction to Deep Learning
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 This video provides a course overview and introduces deep neural networks, covering their fundamental concepts ...
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Deep learning Applications
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Top videos
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Including results for mit deep learning mit opencourseware.
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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
262.7K views4 months ago
YouTubeMIT OpenCourseWare
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 Netw…
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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…
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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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5: Deep Learning for Natural Language – The Basics
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5: Deep Learning for Natural Language – The Basics
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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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7: Deep Learning for Natural Language – Transformers
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7: Deep Learning for Natural Language – Transformers
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Lec 24. Inference Methods for Deep Learning
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PyTorch Tutorial
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6: Deep Learning for Natural Language – Embeddings
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Lec 08. Architectures: Transformers
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Lec 23. Metrized Deep Learning
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Lec 05. Architectures: Graphs
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Lec 03. Approximation Theory
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Lec 14. Generative Models: Basics
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Lec 11. Representation Learning: Reconstruction-Based
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Lec 18. Transfer Learning: Models
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Lec 19. Transfer Learning: Data
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Lec 17. Generalization: Out-of-Distribution (OOD)
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Lec 15. Generative Models: Representation Learning Meets Gene…
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Lec 12. Representation Learning: Similarity-Based
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Lec 21. Language Models
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2: Training Deep NNs (cont.); Introduction to Keras/Tensorflow; Ap…
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10: Generative AI – Adapting LLMs with Parameter-Efficient Fine-Tuning
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8: Deep Learning for Natural Language – Transformers, Self-Supervised Lea…
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Lec 13. Representation Learning: Theory
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Lec 06. Generalization Theory
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Lec 07. Scaling Rules for Optimization
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