📐 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

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Lec 09. Hacker's Guide to Deep Learning

MIT OpenCourseWare Beginner 1mo ago

Lec 16. Generative Models: Conditional Models

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Lec 16. Generative Models: Conditional Models

MIT OpenCourseWare Beginner 1mo ago

Lec 15. Generative Models: Representation Learning Meets Generative Modeling

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Lec 15. Generative Models: Representation Learning Meets Generative Modeling

MIT OpenCourseWare Beginner 1mo ago

Lec 04. Architectures: Grids

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Lec 04. Architectures: Grids

MIT OpenCourseWare Beginner 1mo ago

Lec 02. How to Train a Neural Net

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Lec 02. How to Train a Neural Net

MIT OpenCourseWare Beginner 1mo ago

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

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

MIT OpenCourseWare Beginner 1mo ago

Lec 01. Introduction to Deep Learning

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Lec 01. Introduction to Deep Learning

MIT OpenCourseWare Beginner 1mo ago

Lec 20. Scaling Laws

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Lec 20. Scaling Laws

MIT OpenCourseWare Beginner 1mo ago

Lec 13. Representation Learning: Theory

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Lec 13. Representation Learning: Theory

MIT OpenCourseWare Beginner 1mo ago

Lec 10. Architectures: Memory

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Lec 10. Architectures: Memory

MIT OpenCourseWare Beginner 1mo ago

Lec 11. Representation Learning: Reconstruction-Based

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Lec 11. Representation Learning: Reconstruction-Based

MIT OpenCourseWare Beginner 1mo ago

Lec 14. Generative Models: Basics

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Lec 14. Generative Models: Basics

MIT OpenCourseWare Beginner 1mo ago

Lec 03. Approximation Theory

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Lec 03. Approximation Theory

MIT OpenCourseWare Beginner 1mo ago

Lec 12. Representation Learning: Similarity-Based

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Lec 12. Representation Learning: Similarity-Based

MIT OpenCourseWare Beginner 1mo ago

Lec 07. Scaling Rules for Optimization

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Lec 07. Scaling Rules for Optimization

MIT OpenCourseWare Beginner 1mo ago

Lec 06. Generalization Theory

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Lec 06. Generalization Theory

MIT OpenCourseWare Beginner 1mo ago

PyTorch Tutorial

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PyTorch Tutorial

MIT OpenCourseWare Beginner 1mo ago

Lec 18. Transfer Learning: Models

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Lec 18. Transfer Learning: Models

MIT OpenCourseWare Beginner 1mo ago

5: Deep Learning for Natural Language – The Basics

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5: Deep Learning for Natural Language – The Basics

MIT OpenCourseWare Beginner 3mo ago

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

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

MIT OpenCourseWare Beginner 3mo ago

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; Intro to HuggingFace

MIT OpenCourseWare Beginner 3mo ago

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

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

MIT OpenCourseWare Beginner 3mo ago

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

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

MIT OpenCourseWare Beginner 4mo ago

Lecture 23: Introduction to Machine Learning

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Lecture 23: Introduction to Machine Learning

MIT OpenCourseWare Beginner 4mo ago

Lecture 1, Part I: Introduction of the Class

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Lecture 1, Part I: Introduction of the Class

MIT OpenCourseWare Beginner 4mo ago

Lecture 24: Stochastic Calculus

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Lecture 24: Stochastic Calculus

MIT OpenCourseWare Beginner 4mo ago

Class 27 Video: Feature Extraction and Machine Learning

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Class 27 Video: Feature Extraction and Machine Learning

MIT OpenCourseWare Beginner 5mo ago

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

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

MIT OpenCourseWare Beginner 5mo ago

The Four Fundamental Subspaces and Least Squares

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The Four Fundamental Subspaces and Least Squares

MIT OpenCourseWare Beginner 1y ago

OCW Learning Journeys: Michael's Story

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OCW Learning Journeys: Michael's Story

MIT OpenCourseWare Beginner 1y ago

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

ML Fundamentals

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

MIT OpenCourseWare Beginner 1y ago

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

ML Fundamentals

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

MIT OpenCourseWare Beginner 1y ago

Five Factorizations of a Matrix

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Five Factorizations of a Matrix

MIT OpenCourseWare Beginner 2y ago

Lecture 5 Part 1: Derivative of Matrix Determinant and Inverse

ML Fundamentals

Lecture 5 Part 1: Derivative of Matrix Determinant and Inverse

MIT OpenCourseWare Beginner 2y ago

Lecture 5 Part 2: Forward Automatic Differentiation via Dual Numbers

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Lecture 5 Part 2: Forward Automatic Differentiation via Dual Numbers

MIT OpenCourseWare Beginner 2y ago

Lecture 5 Part 3: Differentiation on Computational Graphs

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Lecture 5 Part 3: Differentiation on Computational Graphs

MIT OpenCourseWare Beginner 2y ago

Lecture 1 Part 1: Introduction and Motivation

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Lecture 1 Part 1: Introduction and Motivation

MIT OpenCourseWare Beginner 2y ago

Lecture 8 Part 1: Derivatives of Eigenproblems

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Lecture 8 Part 1: Derivatives of Eigenproblems

MIT OpenCourseWare Beginner 2y ago

Lecture 3 Part 1: Kronecker Products and Jacobians

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Lecture 3 Part 1: Kronecker Products and Jacobians

MIT OpenCourseWare Beginner 2y ago

Lecture 3 Part 2: Finite-Difference Approximations

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Lecture 3 Part 2: Finite-Difference Approximations

MIT OpenCourseWare Beginner 2y ago

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

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

MIT OpenCourseWare Beginner 2y ago

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

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

MIT OpenCourseWare Beginner 2y ago

Lecture 7 Part 2: Second Derivatives, Bilinear Forms, and Hessian Matrices

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Lecture 7 Part 2: Second Derivatives, Bilinear Forms, and Hessian Matrices

MIT OpenCourseWare Beginner 2y ago

Lecture 4 Part 1: Gradients and Inner Products in Other Vector Spaces

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Lecture 4 Part 1: Gradients and Inner Products in Other Vector Spaces

MIT OpenCourseWare Beginner 2y ago

Lecture 2 Part 2: Vectorization of Matrix Functions

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Lecture 2 Part 2: Vectorization of Matrix Functions

MIT OpenCourseWare Beginner 2y ago

Lecture 1 Part 2: Derivatives as Linear Operators

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Lecture 1 Part 2: Derivatives as Linear Operators

MIT OpenCourseWare Beginner 2y ago

Lecture 8 Part 2: Automatic Differentiation on Computational Graphs

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Lecture 8 Part 2: Automatic Differentiation on Computational Graphs

MIT OpenCourseWare Beginner 2y ago

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Foundations of No-Code AI for Real-World Applications

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Foundations of No-Code AI for Real-World Applications

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Deep learning in Electronic Health Records - CDSS 2

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Practical MATLAB Skills

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Practical MATLAB Skills

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Generative Deep Learning with TensorFlow

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Statistical Learning for Engineering Part 2

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DP-100 Microsoft Azure DS Exam

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DP-100 Microsoft Azure DS Exam

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Math for AI Beginner Part 2 : Vector Calulus

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How to Build an Enterprise AI Strategy

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Python: Mastering NumPy Essentials

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Differential Equations Part II Series Solutions

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Differential Equations Part II Series Solutions

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Machine Learning with Implementation in Java

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Mendeploy dan Menskalakan Model AI dengan Cloud Run

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AI Fundamentals in Financial Services

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