Serrano.Academy Courses

A Friendly Introduction to Recurrent Neural Networks

2 reviews

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

A Friendly Introduction to Machine Learning

1 review

Explore key machine learning algorithms through clear examples, from linear regression to neural networks and clustering techniques, in this beginner-friendly introduction.

• YouTube
• 31 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

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

Decision Trees - A Friendly Introduction

Learn to build and train decision trees through a simple example, exploring key concepts like data splitting, overfitting prevention, and alternative evaluation metrics.

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

What Is Quantum Machine Learning?

Friendly introduction to quantum computing and machine learning, covering qubits, quantum gates, entanglement, and their applications in generative modeling and optimization.

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

Denoising and Variational Autoencoders

Explore autoencoders, powerful generative models for dimensionality reduction and data generation. Learn about denoising and variational autoencoders, their applications, and training techniques.

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

A Friendly Introduction to Deep Reinforcement Learning, Q-Networks and Policy Gradients

Friendly exploration of deep reinforcement learning concepts, including Markov decision processes, Q-networks, and policy gradients, using examples and visuals to explain complex ideas.

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

Singular Value Decomposition and Image Compression

Learn singular value decomposition (SVD) and its application in image compression. Explore matrix transformations, dimensionality reduction, and practical implementation techniques.

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

Restricted Boltzmann Machines - A Friendly Introduction

Friendly introduction to Restricted Boltzmann Machines using real-life examples. Covers key concepts like probabilities, training, contrastive divergence, and Gibbs sampling for machine learning enthusiasts.

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

Training Latent Dirichlet Allocation: Gibbs Sampling - Part 2

Explore Gibbs sampling for training Latent Dirichlet Allocation models, learning to sort documents into topics efficiently through practical exercises and in-depth explanations.

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

Latent Dirichlet Allocation - Part 1

Explore Latent Dirichlet Allocation, a powerful machine learning technique for sorting documents by topic. Learn its principles, applications, and implementation in this comprehensive tutorial.

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

Naive Bayes Classifier - A Friendly Approach

Visual explanation of Bayes' Theorem and Naive Bayes algorithm, applied to spam detection. Accessible approach requiring only basic math skills and a desire to learn.

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

Principal Component Analysis

Explore dimensionality reduction through PCA, covering variance, covariance, eigenvectors, and eigenvalues. Learn key concepts with visual explanations and practical applications in data analysis.

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

Support Vector Machines - A Friendly Introduction

Explore Support Vector Machines through visual explanations, covering key concepts like data separation, perceptron algorithm, classification errors, and the C parameter for optimal model performance.

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