Dive into the world of unsupervised learning through a comprehensive 4.5-hour tutorial covering a wide range of advanced topics. Explore Gaussian Mixture Models, clustering techniques like K-means and Hierarchical clustering, and Principal Component Analysis (PCA). Discover the inner workings of recommendation systems using Matrix Factorization, and delve into Latent Dirichlet Allocation with a two-part explanation including Gibbs Sampling. Gain insights into Restricted Boltzmann Machines, learn about Singular Value Decomposition and its application in image compression, and understand Denoising and Variational Autoencoders. Conclude with a friendly introduction to Generative Adversarial Networks (GANs), equipping yourself with cutting-edge knowledge in unsupervised learning techniques.
Overview
Syllabus
Gaussian Mixture Models.
Clustering: K-means and Hierarchical.
Principal Component Analysis (PCA).
How does Netflix recommend movies? Matrix Factorization.
Latent Dirichlet Allocation (Part 1 of 2).
Training Latent Dirichlet Allocation: Gibbs Sampling (Part 2 of 2).
Restricted Boltzmann Machines (RBM) - A friendly introduction.
Singular Value Decomposition (SVD) and Image Compression.
Denoising and Variational Autoencoders.
A Friendly Introduction to Generative Adversarial Networks (GANs).
Taught by
Serrano.Academy
