Variational Autoencoder (VAE) Courses and Certifications

Coursera

Fine-tuning Image Models with Diffusion

Master diffusion model fine-tuning with LoRA, DreamBooth, and ComfyUI to create custom image generation models for production-quality results.

• Coursera
• 8 hours 5 minutes
• Self-Paced
• Paid Course

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

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

Intro to Generative AI for Business Professionals

374 ratings at Udemy

Unleash the Power of Generative AI for Your Business: A Practical Guide

• Udemy
• 1 hour 58 minutes
• Self-Paced
• Paid Course

TensorFlow 2.0 Practical Advanced

354 ratings at Udemy

Master Tensorflow 2.0, Google’s most powerful Machine Learning Library, with 5 advanced practical projects

• Udemy
• 12 hours 38 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

Towards Unsupervised Biomedical Image Segmentation Using Hyperbolic Representations - Jeffrey Gu

2 ratings

Explore unsupervised biomedical image segmentation using hyperbolic representations. Learn about novel self-supervised hierarchical loss and its applications in medical imaging analysis.

• YouTube
• 1 hour 3 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

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

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

Deep Generative Models - Lecture 6: Variational Autoencoders

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

• YouTube
• 1 hour 22 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