Latent Variable Models Courses and Certifications

Alfredo Canziani

Latent Variable Energy Based Models

Explore Latent Variable Energy Based Models (LV-EBMs) training, covering free energy concepts, loss functionals, and model manifolds. Learn practical implementation using PyTorch and gain insights into decoder learning and latent size selection.

• YouTube
• 1 hour 5 minutes
• Self-Paced
• Free Video

IEEE

Small Covers for Near-zero Sets of Polynomials and Learning Latent Variable Models

Explore techniques for parameter estimation in Gaussian mixture models and mixtures of linear regressions, focusing on dimension reduction and higher moments to overcome obstacles in learning latent variable models.

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

Fundamentals of Generative AI and Large Language Models: Theory and Practice

Explore the theory and practice of Generative AI, covering VAEs, GANs, Diffusion Models, Transformers, and LLMs with mathematical foundations and hands-on implementation skills.

• Swayam
• Self-Paced
• Free Online Course

Alexander Amini;  MIT

Deep Generative Modeling

1 rating

Explore deep generative modeling, including autoencoders, variational autoencoders, and GANs. Learn about latent variable models, reparameterization tricks, and recent advances in generative AI techniques.

• YouTube
• 1 hour 2 minutes
• Self-Paced
• Free Video

Alexander Amini;  MIT

Deep Generative Modeling

Explore deep generative modeling, including autoencoders, variational autoencoders, and GANs. Learn about latent variable models, reparameterization tricks, and recent advances in generative AI.

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

Deep Generative Models - Lecture 5: Variational Autoencoders

Explore Variational Autoencoders (VAEs) in deep generative modeling, covering theoretical foundations and practical applications in this comprehensive Stanford lecture.

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

Can Diffusion Model Disentangle? A Theoretical Perspective

Explore a theoretical framework for understanding how diffusion models learn disentangled representations, with identifiability conditions, training dynamics analysis, and experimental validation across diverse tasks and modalities.

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

Yannic Kilcher

Topographic VAEs Learn Equivariant Capsules - Machine Learning Research Paper Explained

Explore Topographic VAEs: a novel approach to deep generative models with organized latent variables, bridging topographic organization and equivariance in neural networks for improved feature learning and transformation handling.

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

Yannic Kilcher

Self-Supervised Learning - The Dark Matter of Intelligence

Explore self-supervised learning as a promising approach for AI to develop background knowledge and common sense, with insights from Yann LeCun and Ishan Misra of Facebook AI Research.

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

Alexander Amini;  MIT

Deep Generative Modeling

Explore deep generative modeling, including autoencoders, VAEs, and GANs. Learn about latent variables, representation learning, and applications in debiasing and image synthesis.

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

Alexander Amini;  MIT

Deep Generative Modeling

Explore deep generative modeling, including autoencoders, VAEs, and GANs. Learn about latent variables, representation learning, and applications in image generation and domain transformation.

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

Alfredo Canziani

Energy Based Models and Self-Supervised Learning

Explore energy-based models, latent variables, and self-supervised learning. Dive into EBM training, inference, and comparison with probabilistic models. Discover contrastive methods and applications like K-means and BERT.

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

Artem Kirsanov

Hidden Beauty Behind Generative AI: Variational Inference and ELBO

Delve into the mathematical foundations of generative AI through variational inference and ELBO concepts, exploring how intelligent systems model probability distributions effectively.

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

Alfredo Canziani

NYU Deep Learning - Fall 2022

Master neural network inference, energy-based classification, and latent variable models through PyTorch implementations and backpropagation techniques.

• YouTube
• 8 hours 30 minutes
• Self-Paced
• Free Video

Chemometrics & Machine Learning in Copenhagen

Basic Definition of Multivariate Data Analysis - Chemometrics

1 rating

Explore fundamental concepts of multivariate data analysis and chemometrics, including univariate vs. multivariate data, latent variables, and their applications in analytical chemistry.

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