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58 lessons

📐 ML Fundamentals

Neural networks, backpropagation, gradient descent — the maths behind AI

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Lec 05. Architectures: Graphs
📐 ML Fundamentals
Lec 05. Architectures: Graphs
MIT OpenCourseWare Beginner 1mo ago
Lec 09. Hacker's Guide to Deep Learning
📐 ML Fundamentals
Lec 09. Hacker's Guide to Deep Learning
MIT OpenCourseWare Beginner 1mo ago
Lec 16. Generative Models: Conditional Models
📐 ML Fundamentals
Lec 16. Generative Models: Conditional Models
MIT OpenCourseWare Beginner 1mo ago
Lec 15. Generative Models: Representation Learning Meets Generative Modeling
📐 ML Fundamentals
Lec 15. Generative Models: Representation Learning Meets Generative Modeling
MIT OpenCourseWare Beginner 1mo ago
Lec 04. Architectures: Grids
📐 ML Fundamentals
Lec 04. Architectures: Grids
MIT OpenCourseWare Beginner 1mo ago
Lec 02. How to Train a Neural Net
📐 ML Fundamentals
Lec 02. How to Train a Neural Net
MIT OpenCourseWare Beginner 1mo ago
Lec 17. Generalization: Out-of-Distribution (OOD)
📐 ML Fundamentals
Lec 17. Generalization: Out-of-Distribution (OOD)
MIT OpenCourseWare Beginner 1mo ago
Lec 01. Introduction to Deep Learning
📐 ML Fundamentals
Lec 01. Introduction to Deep Learning
MIT OpenCourseWare Beginner 1mo ago
Lec 20. Scaling Laws
📐 ML Fundamentals
Lec 20. Scaling Laws
MIT OpenCourseWare Beginner 1mo ago
Lec 13. Representation Learning: Theory
📐 ML Fundamentals
Lec 13. Representation Learning: Theory
MIT OpenCourseWare Beginner 1mo ago
Lec 10. Architectures: Memory
📐 ML Fundamentals
Lec 10. Architectures: Memory
MIT OpenCourseWare Beginner 1mo ago
Lec 11. Representation Learning: Reconstruction-Based
📐 ML Fundamentals
Lec 11. Representation Learning: Reconstruction-Based
MIT OpenCourseWare Beginner 1mo ago
Lec 14. Generative Models: Basics
📐 ML Fundamentals
Lec 14. Generative Models: Basics
MIT OpenCourseWare Beginner 1mo ago
Lec 03. Approximation Theory
📐 ML Fundamentals
Lec 03. Approximation Theory
MIT OpenCourseWare Beginner 1mo ago
Lec 12. Representation Learning: Similarity-Based
📐 ML Fundamentals
Lec 12. Representation Learning: Similarity-Based
MIT OpenCourseWare Beginner 1mo ago
Lec 07. Scaling Rules for Optimization
📐 ML Fundamentals
Lec 07. Scaling Rules for Optimization
MIT OpenCourseWare Beginner 1mo ago
Lec 06. Generalization Theory
📐 ML Fundamentals
Lec 06. Generalization Theory
MIT OpenCourseWare Beginner 1mo ago
PyTorch Tutorial
📐 ML Fundamentals
PyTorch Tutorial
MIT OpenCourseWare Beginner 1mo ago
Lec 18. Transfer Learning: Models
📐 ML Fundamentals
Lec 18. Transfer Learning: Models
MIT OpenCourseWare Beginner 1mo ago
5: Deep Learning for Natural Language – The Basics
📐 ML Fundamentals
5: Deep Learning for Natural Language – The Basics
MIT OpenCourseWare Beginner 2mo ago
2: Training Deep NNs (cont.); Introduction to Keras/Tensorflow; Application to Tabular Data
📐 ML Fundamentals
2: Training Deep NNs (cont.); Introduction to Keras/Tensorflow; Application to Tabular Data
MIT OpenCourseWare Beginner 2mo ago
4: Deep Learning for Computer Vision – Transfer Learning and Fine-Tuning; Intro to HuggingFace
📐 ML Fundamentals
4: Deep Learning for Computer Vision – Transfer Learning and Fine-Tuning; Intro to HuggingFace
MIT OpenCourseWare Beginner 2mo ago
1: Introduction to Neural Networks and Deep Learning; Training Deep NNs
📐 ML Fundamentals
1: Introduction to Neural Networks and Deep Learning; Training Deep NNs
MIT OpenCourseWare Beginner 2mo ago
Lecture 4: Linear Algebra (cont.); Probability Theory
📐 ML Fundamentals
Lecture 4: Linear Algebra (cont.); Probability Theory
MIT OpenCourseWare Beginner 3mo ago