Where can I find mathematical derivations for gradient descent loss functions?

Entering optimization terms takes you directly to video clips where instructors write out gradient calculations and optimization equations on board.

Is it possible to search transformer self-attention mechanisms in deep learning videos?

Yes. Querying transformer architecture concepts scans advanced machine learning lectures to extract segments explaining attention matrices.

Does this index cover computer vision and reinforcement learning algorithms?

The AI index catalogs convolutional neural networks, policy gradient methods, generative adversarial networks, and large language model mechanics.

AI & Machine Learning Video Subtitle Finder - Page 3

🧠1:21:45

Adversarial Examples & Training in Deep Learning | Stanford Lecture 16

@stanfordengineering

2017-08-11

224.7K

🧠1:18:20

CS231n Lecture 9: Visualizing CNNs, Deep Dream, Neural Style & Adversarial Examples

@andrejkarpathy4906

2016-02-04

66.4K

🎬8:37

Convolutional Neural Networks Explained | CNN Tutorial for Deep Learning

@deeplizard

2017-12-09

1.5M

🧠42:27

Flocking Simulation with Boids Algorithm in JavaScript and p5.js

@TheCodingTrain

2018-12-11

359.3K

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The Mathematical Secrets of Bird Flocking and Murmurations

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2020-04-27

991.6K

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@lexfridman

2018-04-25

383.2K

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@sentdex

2019-06-06

116.5K

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Deep Reinforcement Learning Lecture: MDPs, Q-Learning, and Policy Gradients

@stanfordengineering

2017-08-11

384.7K

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Knowledge Graphs in the Enterprise | EY AI & Graph Technology

@neo4j

2018-01-05

18.5K

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Graph Neural Networks for Molecular Design and Drug Discovery

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@lindseyai4843

2020-06-18

142.5K

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149.7K

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2021-08-23

261.4K

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AI in Conservation: Protecting Birds from Wind Turbines

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2026-02-23

148

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Build a Low Latency Local Speech-to-Speech System with Open-Source AI

@AllAboutAI

2024-01-11

157.8K

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2025-10-06

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@Dev-Spot

2023-12-26

19.2K

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Precision, Recall, and F1 Score for Multiclass Classification in Python

@regenerativetoday4244

2022-06-09

12.9K

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Stanford CS229: Bias-Variance Tradeoff, Cross-Validation & Regularization | Lecture 12

@stanfordonline

2021-04-20

20.6K

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Evaluation and Cross-Validation in Machine Learning

@machinelearning-sudeshnasa3607

2016-06-28

156.5K

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K-Nearest Neighbor Algorithm: Classification & Regression Guide

@IBMTechnology

2024-09-02

106.1K

🧠18:08

Decision Trees and Classification Trees Explained (Machine Learning Basics)

@statquest

2021-04-26

1.1M

🎬9:54

Random Forests Explained: Build, Use & Evaluate ML Models

@statquest

2018-02-05

1.4M

🧠28:25

Gradient Descent & Cost Function Tutorial for Linear Regression Python

@codebasics

2018-07-22

805.9K

🧠30:00

Matrix Algebra for Neural Networks Clearly Explained in Mathematics

@statquest

2023-12-11

78.3K

🎬8:47

Logistic Regression Explained: A Beginner's Guide to Classification

@statquest

2018-03-05

2.6M

🧠27:51

Machine Learning and AI Concepts Explained with Animations

@freecodecamp

2025-04-22

241.3K

🧠1:22:29

Backpropagation & Neural Networks | Stanford CS224N Lecture 3

@stanfordonline

2021-10-29

117.8K

🧠17:59

Linear Regression Gradient Descent Explained | Machine Learning Tutorial

@MachineLearningWithJay

2020-07-26

112.4K

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Text Preprocessing for NLP: Tokenization, Stemming & Lemmatization

@activelearning4386

2022-12-10

642

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Model Interpretability with LIME & SHAP | Data Science Tutorial

@PyDataTV

2019-02-01

40.5K

🧠13:58

Explainable AI: LIME for Model Interpretability

@DeepFindr

2021-02-25

82.8K

🧠1:12:37

Explainable AI: Post Hoc Methods for Model Interpretability

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2022-11-04

16.5K

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Shapley Values in Machine Learning: Feature Attribution

@adataodyssey

2023-04-03

30.5K

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Machine Learning Explainability: Inherently Interpretable Models (Stanford Seminar)

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2022-11-03

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2023-04-29

687

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Explainable AI Explained: Introduction to XAI & Interpretable Models

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2021-02-08

89.1K

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Fairness in Machine Learning: Fairlearn Tutorial Guide

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2021-11-25

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🧠10:32

Fairness Metrics in Machine Learning: Equal Opportunity & More

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2023-07-30

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AI, Machine Learning & Deep Learning Subtitle Finder - Page 3

Adversarial Examples & Training in Deep Learning | Stanford Lecture 16

CS231n Lecture 9: Visualizing CNNs, Deep Dream, Neural Style & Adversarial Examples

Convolutional Neural Networks Explained | CNN Tutorial for Deep Learning

Flocking Simulation with Boids Algorithm in JavaScript and p5.js

The Mathematical Secrets of Bird Flocking and Murmurations

Ilya Sutskever on Meta-Learning and Self-Play at MIT AGI

Building a Custom Reinforcement Learning Environment with Q-Learning

Deep Reinforcement Learning Lecture: MDPs, Q-Learning, and Policy Gradients

Knowledge Graphs in the Enterprise | EY AI & Graph Technology

Graph Neural Networks for Molecular Design and Drug Discovery

Predicting Lipophilicity from SMILES Strings with PyTorch Geometric

Graph Neural Networks (GNN) with PyTorch Geometric | Stanford CS224W

Traditional Node Features in Graph ML | CS224W Lecture 2.1

Graph Neural Networks Explained: From Graphs to GNNs

AI in Conservation: Protecting Birds from Wind Turbines

Build a Low Latency Local Speech-to-Speech System with Open-Source AI

Offline AI Voice Assistant Pipeline: Whisper, LM Studio, and Coqui TTS

Coqui TTS Setup: Local Voice Cloning with Web UI

Precision, Recall, and F1 Score for Multiclass Classification in Python

Stanford CS229: Bias-Variance Tradeoff, Cross-Validation & Regularization | Lecture 12

Evaluation and Cross-Validation in Machine Learning

K-Nearest Neighbor Algorithm: Classification & Regression Guide

Decision Trees and Classification Trees Explained (Machine Learning Basics)

Random Forests Explained: Build, Use & Evaluate ML Models

Gradient Descent & Cost Function Tutorial for Linear Regression Python

Matrix Algebra for Neural Networks Clearly Explained in Mathematics

Logistic Regression Explained: A Beginner's Guide to Classification

Machine Learning and AI Concepts Explained with Animations

Backpropagation & Neural Networks | Stanford CS224N Lecture 3

Linear Regression Gradient Descent Explained | Machine Learning Tutorial

Text Preprocessing for NLP: Tokenization, Stemming & Lemmatization

Model Interpretability with LIME & SHAP | Data Science Tutorial

Explainable AI: LIME for Model Interpretability

Explainable AI: Post Hoc Methods for Model Interpretability

Shapley Values in Machine Learning: Feature Attribution

Machine Learning Explainability: Inherently Interpretable Models (Stanford Seminar)

Interpreting Black Box Models in Machine Learning: A Guide

Explainable AI Explained: Introduction to XAI & Interpretable Models

Fairness in Machine Learning: Fairlearn Tutorial Guide

Fairness Metrics in Machine Learning: Equal Opportunity & More