17:41

What is AdaBoost? Friendly explanation with code!

09:50

How do Transformer Models keep track of the order of words? Positional Encoding

34:54

Model that won the 2024 Physics Nobel Prize - Hopfield Networks

17:27

The Fast Fourier Transform

17:51

When is a sequence periodic? The Discrete Fourier Transform will tell us

17:27

The Discrete Fourier Transform

26:06

State Space Models (SSMs) and Mamba

21:15

Direct Preference Optimization (DPO) - How to fine-tune LLMs directly without reinforcement learning

13:48

KL Divergence - How to tell how different two distributions are

37:23

Why do we divide by n-1 to estimate the variance? A visual tour through Bessel correction

15:31

Reinforcement Learning with Human Feedback - How to train and fine-tune Transformer Models

38:24

Proximal Policy Optimization (PPO) - How to train Large Language Models

44:59

Stable Diffusion - How to build amazing images with AI

44:26

What are Transformer Models and how do they work?

36:16

The math behind Attention: Keys, Queries, and Values matrices

21:02

The Attention Mechanism in Large Language Models

26:41

The Binomial and Poisson Distributions

25:18

Euler's number, derivatives, and the bank at the end of the universe

22:23

Decision trees - A friendly introduction

15:04

How do you minimize a function when you can't take derivatives? CMA-ES and PSO

51:32

What is Quantum Machine Learning?

31:46

Denoising and Variational Autoencoders

19:18

Eigenvectors and Generalized Eigenspaces

12:40

Thompson sampling, one armed bandits, and the Beta distribution

13:31

The Beta distribution in 12 minutes!

36:26

A friendly introduction to deep reinforcement learning, Q-networks and policy gradients

08:39

The Gini Impurity Index explained in 8 minutes!

13:57

The covariance matrix

17:27

Gaussian Mixture Models

28:56

Singular Value Decomposition (SVD) and Image Compression

10:54

ROC (Receiver Operating Characteristic) Curve in 10 minutes!

36:58

Generative model that won the 2024 Physics Nobel Prize - Restricted Boltzmann Machines (RBM)

21:01

A Friendly Introduction to Generative Adversarial Networks (GANs)

10:36

You are much better at math than you think

26:31

Training Latent Dirichlet Allocation: Gibbs Sampling (Part 2 of 2)

26:57

Latent Dirichlet Allocation (Part 1 of 2)

01:01

Book by Luis Serrano - "Grokking Machine Learning" (40% off promo code)

00:42

Serrano.Academy - The art of understanding

20:29

Naive Bayes classifier: A friendly approach

11:01

Math and OCD - My story with the Thue-Morse sequence

26:34

Principal Component Analysis (PCA)

17:23

Clustering: K-means and Hierarchical

30:58

Support Vector Machines (SVMs): A friendly introduction

45:19

Logistic Regression and the Perceptron Algorithm: A friendly introduction

31:05

Linear Regression: A friendly introduction

32:46

How does Netflix recommend movies? Matrix Factorization

57:49

Deep Neural Networks - USF Data Science Seminar by Luis Serrano

32:46

A friendly introduction to Bayes Theorem and Hidden Markov Models

21:16

Shannon Entropy and Information Gain

22:44

A friendly introduction to Recurrent Neural Networks

32:08

A friendly introduction to Convolutional Neural Networks and Image Recognition

44:43

Machine Learning: Testing and Error Metrics

33:20

A friendly introduction to Deep Learning and Neural Networks

30:49

A Friendly Introduction to Machine Learning

14:45

Bayes theorem, false positives, and why I'm terrified of going to the doctor

09:15

Geometric series and my Irish heritage