📐 ML Fundamentals

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

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18: Object Recognition (cont'd), Texture Perception

ML Fundamentals

18: Object Recognition (cont'd), Texture Perception

MIT OpenCourseWare Intermediate 1w ago

Session 11: Gas Exchange Across the Air-sea Interface

ML Fundamentals

Session 11: Gas Exchange Across the Air-sea Interface

MIT OpenCourseWare Intermediate 2w ago

Session 17: Sinking Particles and Remineralization (2)

ML Fundamentals

Session 17: Sinking Particles and Remineralization (2)

MIT OpenCourseWare Intermediate 2w ago

Session 10: Non-conservative Processes in Estuaries/ Groundwater/Hydrothermal

ML Fundamentals

Session 10: Non-conservative Processes in Estuaries/ Groundwater/Hydrothermal

MIT OpenCourseWare Intermediate 2w ago

Session 14: Primary Production (2)

ML Fundamentals

Session 14: Primary Production (2)

MIT OpenCourseWare Intermediate 2w ago

A Critical Lens on Science Images

ML Fundamentals

A Critical Lens on Science Images

MIT OpenCourseWare Intermediate 2w ago

A Critical Lens on Science Images

ML Fundamentals

A Critical Lens on Science Images

MIT OpenCourseWare Intermediate 3w ago

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 23. Metrized Deep Learning

ML Fundamentals

Lec 23. Metrized Deep Learning

MIT OpenCourseWare Advanced 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

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

ML Fundamentals

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

MIT OpenCourseWare Advanced 2mo 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 4mo ago

Lecture 23: Introduction to Machine Learning

ML Fundamentals

Lecture 23: Introduction to Machine Learning

MIT OpenCourseWare Beginner 4mo ago

Lecture 1, Part I: Introduction of the Class

ML Fundamentals

Lecture 1, Part I: Introduction of the Class

MIT OpenCourseWare Beginner 4mo ago

Lecture 2: Linear Algebra

ML Fundamentals

Lecture 2: Linear Algebra

MIT OpenCourseWare Advanced 4mo ago

Lecture 24: Stochastic Calculus

ML Fundamentals

Lecture 24: Stochastic Calculus

MIT OpenCourseWare Beginner 4mo ago

Lecture 25: Stochastic Calculus (cont.); Stochastic Differential Equations

ML Fundamentals

Lecture 25: Stochastic Calculus (cont.); Stochastic Differential Equations

MIT OpenCourseWare Advanced 4mo ago

Class 27 Video: Feature Extraction and Machine Learning

ML Fundamentals

Class 27 Video: Feature Extraction and Machine Learning

MIT OpenCourseWare Beginner 5mo ago

Class 28 Video: Feature Extraction and Machine Learning (II)

ML Fundamentals

Class 28 Video: Feature Extraction and Machine Learning (II)

MIT OpenCourseWare Advanced 5mo ago

Video 29a: Feature Extraction and Machine Learning (III): Artificial Intelligence

ML Fundamentals

Video 29a: Feature Extraction and Machine Learning (III): Artificial Intelligence

MIT OpenCourseWare Beginner 5mo ago

MIT Economist Explains the NFL’s Legal Cartel

ML Fundamentals

MIT Economist Explains the NFL’s Legal Cartel

MIT OpenCourseWare Intermediate 7mo ago

Lecture 19: Fundamental Theorem of Calculus

ML Fundamentals

Lecture 19: Fundamental Theorem of Calculus

MIT OpenCourseWare Intermediate 7mo ago

Lecture 5: Sums

ML Fundamentals

Lecture 5: Sums

MIT OpenCourseWare Advanced 8mo ago

“Why so many cereals?" - The economics of competition

ML Fundamentals

“Why so many cereals?" - The economics of competition

MIT OpenCourseWare Beginner 8mo ago

LeBron vs. Lawncare: Why It’s All About Trade-Offs

ML Fundamentals

LeBron vs. Lawncare: Why It’s All About Trade-Offs

MIT OpenCourseWare Intermediate 9mo ago

What is "rubber duck debugging?"

ML Fundamentals

What is "rubber duck debugging?"

MIT OpenCourseWare Beginner 10mo ago

What is "rubber duck debugging?"

ML Fundamentals

What is "rubber duck debugging?"

MIT OpenCourseWare Beginner 10mo ago