Variational Autoencoders Courses and Certifications

Edureka

Generative AI Models and GPU Systems

Master generative AI by exploring VAEs, GANs, and diffusion models, then scale and deploy them using GPU optimization, mixed precision, and distributed training strategies.

• Coursera
• 13 hours 16 minutes
• Self-Paced
• Paid Course

Stanford CS236 - Deep Generative Models I 2023

Master probabilistic foundations and algorithms for deep generative models including VAEs, GANs, normalizing flows, and diffusion models with applications across AI domains.

• YouTube
• 23 hours 38 minutes
• Self-Paced
• Free Video

Paul Hand

Variational Autoencoders

Explore generative models, autoencoders, and variational techniques in deep learning. Learn about architecture, optimization, and practical applications of variational autoencoders.

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

Generative AI Essentials - Practical Use Cases

427 ratings at Udemy

Learn Generative AI Essentials with lectures, quizzes, real world use cases with hands-on demos and exercises

• Udemy
• 4 hours 22 minutes
• Self-Paced
• Paid Course

Generative AI in Healthcare: Practical & Hands-on Learning

344 ratings at Udemy

Build AI Models for Medical Imaging, Disease Prediction, and Patient Care

• Udemy
• 2 hours 44 minutes
• Self-Paced
• Paid Course

Build a Stable Diffusion VAE From Scratch Using PyTorch

Master building Variational Autoencoders (VAE) for image generation using PyTorch, focusing on implementation techniques for latent diffusion models and hands-on neural network training.

• freeCodeCamp
• 43 minutes
• Self-Paced
• Free Video

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

Alfredo Canziani

NYU Deep Learning - Spring 2020

Master neural networks, CNNs, RNNs, GANs, and transformers through hands-on PyTorch implementation covering optimization, energy-based models, and applications in CV and NLP.

• YouTube
• 1 day 17 hours 12 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

Alfredo Canziani

Contrastive Methods and Regularised Latent Variable Models

Explore contrastive methods and regularized latent variable models in energy-based models, covering self-supervised learning, autoencoders, sparse coding, and variational techniques for advanced machine learning applications.

• YouTube
• 1 hour 39 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

Yannic Kilcher

NVAE- A Deep Hierarchical Variational Autoencoder

Detailed explanation of NVAE, a deep hierarchical variational autoencoder for high-resolution image generation, covering architecture, training techniques, and state-of-the-art results.

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

MLCon | Machine Learning Conference

Unsupervised Learning with Autoencoders

Explore autoencoder variants, their applications in unsupervised learning, and real-world use cases. Learn about anomaly detection, denoising, and generative models in this comprehensive overview.

• YouTube
• 49 minutes
• Self-Paced
• Conference Talk

Alexander Amini

Deep Generative Modeling - MIT 6.S191 Lecture 4

Explore deep generative modeling, including autoencoders, VAEs, GANs, and recent advances. Learn about latent variables, training techniques, and applications in AI-driven content creation and data analysis.

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