📐 ML Fundamentals

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

ML Fundamentals

Lec 05. Architectures: Graphs

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ML Fundamentals

Lec 09. Hacker's Guide to Deep Learning

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ML Fundamentals

Lec 16. Generative Models: Conditional Models

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ML Fundamentals

Lec 15. Generative Models: Representation Learning Meets Generative Modeling

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ML Fundamentals

Lec 04. Architectures: Grids

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Lec 02. How to Train a Neural Net

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ML Fundamentals

Lec 17. Generalization: Out-of-Distribution (OOD)

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ML Fundamentals

Lec 01. Introduction to Deep Learning

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ML Fundamentals

Lec 20. Scaling Laws

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ML Fundamentals

Lec 13. Representation Learning: Theory

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ML Fundamentals

Lec 10. Architectures: Memory

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ML Fundamentals

Lec 11. Representation Learning: Reconstruction-Based

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ML Fundamentals

Lec 14. Generative Models: Basics

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ML Fundamentals

Lec 03. Approximation Theory

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ML Fundamentals

Lec 12. Representation Learning: Similarity-Based

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ML Fundamentals

Lec 07. Scaling Rules for Optimization

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ML Fundamentals

Lec 06. Generalization Theory

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ML Fundamentals

PyTorch Tutorial

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ML Fundamentals

Lec 18. Transfer Learning: Models

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ML Fundamentals

5: Deep Learning for Natural Language – The Basics

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ML Fundamentals

2: Training Deep NNs (cont.); Introduction to Keras/Tensorflow; Application to Tabular Data

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ML Fundamentals

4: Deep Learning for Computer Vision – Transfer Learning and Fine-Tuning; Intro to HuggingFace

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ML Fundamentals

1: Introduction to Neural Networks and Deep Learning; Training Deep NNs

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ML Fundamentals

Lecture 4: Linear Algebra (cont.); Probability Theory

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ML Fundamentals

Lecture 23: Introduction to Machine Learning

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ML Fundamentals

Lecture 1, Part I: Introduction of the Class

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ML Fundamentals

Lecture 24: Stochastic Calculus

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ML Fundamentals

Class 27 Video: Feature Extraction and Machine Learning

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ML Fundamentals

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

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ML Fundamentals

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

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ML Fundamentals

What is "rubber duck debugging?"

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ML Fundamentals

What is "rubber duck debugging?"

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ML Fundamentals

Navigating OER Space: MIT Libraries Open Educational Resources Guide

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ML Fundamentals

The Four Fundamental Subspaces and Least Squares

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ML Fundamentals

OCW Learning Journeys: Michael's Story

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ML Fundamentals

OCW Learning Journeys: Jae-Min's Story #Chemistry #Thermodynamics #Physics #Science #MIT

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ML Fundamentals

OCW Learning Journeys: Emmanuel's Story #ArtificialIIntelligence #AI #Psychology #AnimalBehavior

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ML Fundamentals

OCW Learning Journeys: Maria's Story #MIT #Calculus #STEM #Brazil #OpenEducation #MedicalSchool

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ML Fundamentals

Five Factorizations of a Matrix

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ML Fundamentals

Lecture 5 Part 1: Derivative of Matrix Determinant and Inverse

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ML Fundamentals

Lecture 5 Part 2: Forward Automatic Differentiation via Dual Numbers

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ML Fundamentals

Lecture 5 Part 3: Differentiation on Computational Graphs

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ML Fundamentals

Lecture 1 Part 1: Introduction and Motivation

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ML Fundamentals

Lecture 8 Part 1: Derivatives of Eigenproblems

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ML Fundamentals

Lecture 3 Part 1: Kronecker Products and Jacobians

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ML Fundamentals

Lecture 3 Part 2: Finite-Difference Approximations

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ML Fundamentals

Lecture 2 Part 1: Derivatives in Higher Dimensions: Jacobians and Matrix Functions

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ML Fundamentals

Lecture 4 Part 2: Nonlinear Root Finding, Optimization, and Adjoint Gradient Methods

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