Serrano.Academy Courses

A Friendly Introduction to Recurrent Neural Networks

1 review

Explore how computers predict and generate sequences using Recurrent Neural Networks. Learn about vectors, neural networks, and practical applications in cooking, weather, and more.

• YouTube
• 23 minutes
• On-Demand
• Free Video

Machine learning en espanol

Introducción práctica al aprendizaje automático en español, cubriendo técnicas supervisadas y no supervisadas con aplicaciones reales como reconocimiento de imágenes y sistemas de recomendación.

• YouTube
• 3 hours 30 minutes
• On-Demand
• Free Video

Natural Language Processing

Explore Latent Dirichlet Allocation through a two-part series, covering its fundamentals and training using Gibbs Sampling.

• YouTube
• 30 minutes
• On-Demand
• Free Video

Probability

Explore Bayes Theorem, Hidden Markov Models, Shannon Entropy, Naive Bayes classifier, Beta distribution, and Thompson sampling in this friendly introduction to key probability concepts.

• YouTube
• 1 hour 30 minutes
• On-Demand
• Free Video

Generative Models

Explore denoising autoencoders, VAEs, GANs, and RBMs in this comprehensive introduction to generative models, covering key concepts and applications in machine learning.

• YouTube
• 1 hour 30 minutes
• On-Demand
• Free Video

Unsupervised Learning

Explore key unsupervised learning techniques including clustering, dimensionality reduction, and generative models. Gain insights into real-world applications like recommendation systems and image compression.

• YouTube
• 4 hours 30 minutes
• On-Demand
• Free Video

Neural Networks

This Course is a friendly introduction series to machine learning, deep learning, neural networks and generative adversarial networks.

• YouTube
• 3 hours 30 minutes
• On-Demand
• Free Video

General Machine Learning

Explore key machine learning concepts, algorithms, and evaluation methods in this comprehensive introduction to the field.

• YouTube
• 3 hours 30 minutes
• On-Demand
• Free Video

Machine Learning

Comprehensive introduction to key machine learning concepts, algorithms, and applications, covering testing, error metrics, recommendation systems, and various classification methods.

• YouTube
• 3 hours 30 minutes
• On-Demand
• Free Video

Supervised Learning

Explore linear regression, logistic regression, perceptron algorithm, and support vector machines in this friendly introduction to key supervised learning concepts.

• YouTube
• 1 hour 30 minutes
• On-Demand
• Free Video

Transformer Models: Understanding Their Architecture and Functionality - Part 3

Demystifying Transformer models with visuals and examples, covering key concepts like attention mechanism, tokenization, embeddings, and fine-tuning for various NLP tasks.

• YouTube
• 44 minutes
• On-Demand
• Free Video

Direct Preference Optimization - Fine-Tuning LLMs Without Reinforcement Learning

Explore Direct Preference Optimization (DPO), an efficient method for training Large Language Models without reinforcement learning. Learn about the Bradley-Terry model, KL divergence, and loss function.

• YouTube
• 21 minutes
• On-Demand
• Free Video

The Math Behind Attention Mechanisms

Demystify attention mechanisms in Transformer models through visuals, examples, and mathematical explanations, focusing on embeddings, similarity, and key-query-value matrices.

• YouTube
• 38 minutes
• On-Demand
• 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
• On-Demand
• Free Video

The Binomial and Poisson Distributions

Explore binomial and Poisson distributions, from basic concepts to practical applications in probability calculations for real-world scenarios.

• YouTube
• 27 minutes
• On-Demand
• Free Video