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
Restricted Boltzmann Machines (RBM) - A friendly introduction
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
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About
Welcome to Serrano.Academy! I'm Luis Serrano and I love demystifying concepts, capturing their essence, and sharing these videos with you. I prefer illustrations, analogies, and cartoons, rather than formulas (although we don't shy away from the math when needed).
The topics I have are machine learning, mathematics (probability and statistics), but I'm open to many others. If you have any topics you'd like to suggest, feel free to add them in the comments or drop me a line!
For more information, check out serrano.academy.
And also check out my book! Grokking Machine Learning
manning.com/books/grokking-machine-learning
(40% discount code: serranoyt)
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