Serrano.Academy Courses

Training Latent Dirichlet Allocation: Gibbs Sampling - Part 2

Learn to train Latent Dirichlet Allocation by iteratively assigning topic labels to words with Gibbs sampling, without relying on word definitions.

• YouTube
• 27 minutes
• Self-Paced
• Free Video

Latent Dirichlet Allocation - Part 1

An intuitive, geometric introduction to how Latent Dirichlet Allocation represents documents and topics with Dirichlet distributions.

• YouTube
• 27 minutes
• Self-Paced
• Free Video

Principal Component Analysis

A visual, conceptual introduction to PCA that uses variance, covariance, eigenvalues, and eigenvectors to explain dimensionality reduction.

• YouTube
• 27 minutes
• Self-Paced
• Free Video

Support Vector Machines - A Friendly Introduction

A visual, low-math introduction to SVMs that builds a maximum-margin classifier from the perceptron algorithm and explains how the C parameter balances errors.

• YouTube
• 31 minutes
• Self-Paced
• Free Video

Logistic Regression and the Perceptron Algorithm - A Friendly Introduction

A visual, low-math introduction to logistic regression and the perceptron algorithm using email spam classification, decision boundaries, sigmoid activation, and log-loss.

• YouTube
• 45 minutes
• Self-Paced
• Free Video

Linear Regression - A Friendly Introduction

A visual, low-math introduction to fitting lines to data and using linear regression to estimate values.

• YouTube
• 31 minutes
• Self-Paced
• Free Video

How Does Netflix Recommend Movies? Matrix Factorization

Explains how Netflix-style recommenders use matrix factorization to uncover user and movie features, minimize rating error, and predict unseen ratings.

• YouTube
• 33 minutes
• Self-Paced
• Free Video

Shannon Entropy and Information Gain

A visual introduction to Shannon entropy through random draws, probability distributions, logarithms, and the average questions needed to identify an outcome.

• YouTube
• 21 minutes
• Self-Paced
• Free Video

A Friendly Introduction to Convolutional Neural Networks and Image Recognition

A friendly visual explanation of how convolutional neural networks use filters and pooling to recognize images, from simple pixel patterns to objects and faces.

• YouTube
• 32 minutes
• Self-Paced
• Free Video

Machine Learning - Testing and Error Metrics

Learn to evaluate and improve machine learning models with train/test splits, cross-validation, classification metrics, bias-variance analysis, and grid search.

• YouTube
• 45 minutes
• Self-Paced
• Free Video

A Friendly Introduction to Deep Learning and Neural Networks

A visual introduction to gradient descent, logistic regression, activation functions, and deep neural networks through intuitive geometric examples.

• YouTube
• 33 minutes
• Self-Paced
• Free Video

Detecting Periodicity with the Discrete Fourier Transform

Discover how to detect sequence periodicity and find periods using the Discrete Fourier Transform in this informative video on signal processing concepts.

• YouTube
• 18 minutes
• Self-Paced
• Free Video

Stable Diffusion - Building Amazing Images with AI

Explore Stable Diffusion AI for image creation, covering embeddings, diffusion models, and practical examples. Learn to generate stunning visuals from text prompts.

• YouTube
• 45 minutes
• Self-Paced
• Free Video

The Fast Fourier Transform - Efficient Algorithm for Signal Processing

Dive into the Fast Fourier Transform, exploring its efficiency over the Discrete Fourier Transform and understanding the crucial Butterfly algorithm in signal processing.

• YouTube
• 17 minutes
• Self-Paced
• Free Video

Why We Divide by n-1 to Estimate Variance - A Visual Tour Through Bessel's Correction

Explore the visual explanation of Bessel's correction in variance estimation, understanding its importance and mathematical foundations for accurate statistical analysis.

• YouTube
• 37 minutes
• Self-Paced
• Free Video