Generative Models Courses and Certifications

Yannic Kilcher

Set Distribution Networks - A Generative Model for Sets of Images

Explores a novel framework for generating and encoding sets of images, introducing Set Distribution Networks that can create new object/identity sets while preserving key attributes.

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

Valence Labs

Multiflow: Protein Structure and Sequence Co-generation

Explore protein structure and sequence co-generation using Discrete Flow Models and Multiflow, a novel approach combining discrete and continuous data for improved AI-driven drug discovery.

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

Valence Labs

SE(3)-Stochastic Flow Matching for Protein Backbone Generation

Explore novel generative models for protein backbone design using SE(3)-stochastic flow matching. Learn about FoldFlow variants, their advantages, and applications in computational protein structure generation.

• YouTube
• 1 hour 12 minutes
• On-Demand
• Free Video

Valence Labs

Stochastic Interpolants: A Unifying Framework for Flows and Diffusions - Lecture

Explore a unified framework for generative models, bridging flow-based and diffusion-based methods using stochastic interpolants. Learn about probability flow equations, likelihood estimation, and applications in AI for drug discovery.

• YouTube
• 1 hour 40 minutes
• On-Demand
• Free Video

Generative Memory Lab

Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution

Explore discrete diffusion modeling techniques for estimating data distribution ratios, enhancing generative AI capabilities and understanding.

• YouTube
• 1 hour 21 minutes
• On-Demand
• Free Video

Generative Memory Lab

Building Normalizing Flows with Stochastic Interpolants

Explore advanced techniques for constructing normalizing flows using stochastic interpolants, enhancing generative modeling capabilities.

• YouTube
• 1 hour 6 minutes
• On-Demand
• Free Video

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
• On-Demand
• Free Video

Deep Generative Models - Diffusion Models for Discrete Data - Lecture 18

Explore diffusion models for discrete data in this Stanford lecture, covering advanced techniques in deep generative modeling for AI applications.

• YouTube
• 1 hour
• On-Demand
• Free Video

DeepLearning.AI;  Amazon Web Services

Generative AI with Large Language Models

3585 ratings at Coursera

Explore generative AI fundamentals, LLM architecture, and real-world applications. Learn to optimize, fine-tune, and deploy models while addressing challenges and opportunities in this emerging field.

• Coursera
• 16 hours 23 minutes
• On-Demand
• Paid Course

Institute for Pure & Applied Mathematics (IPAM)

STFT Phase Retrieval - Robustness and Generative Priors - IPAM at UCLA

Explore STFT phase retrieval, focusing on robustness and generative priors. Learn about stable reconstruction techniques, application of generative models, and reducing required measurements in imaging and acoustics applications.

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

Society for Industrial and Applied Mathematics

A Data-driven Market Simulator for Small Data Environments - SIAM FME Virtual Talk

Explore data-driven market simulation using deep learning, focusing on small data environments. Learn about generative models, rough paths, and their applications in financial time series analysis.

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

Alan Turing Institute

Convergence of Denoising Diffusion Models Under the Manifold Hypothesis

Explores theoretical analysis of denoising diffusion models, providing convergence results for data distributions on lower-dimensional manifolds or empirical distributions, with quantitative bounds on Wasserstein distance.

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

Computational Genomics Summer Institute CGSI

Recent Advances in Normalizing Flows - CGSI 2023

Explore recent advancements in normalizing flows, including training from dependent data and kernelized approaches, to enhance generative modeling and density estimation techniques.

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

Valence Labs

Discrete Flow Matching for High-Dimensional Discrete Data Generation

Explore Discrete Flow Matching for generating high-dimensional discrete data. Learn about probability paths, sampling techniques, and improved generative perplexity in AI applications.

• YouTube
• 1 hour 34 minutes
• On-Demand
• Free Video

Valence Labs

Metric Flow Matching for Smooth Interpolations on the Data Manifold

Explore Metric Flow Matching for smooth interpolations on data manifolds. Learn about conditional paths, Riemannian metrics, and geodesic interpolants. Discover applications in LiDAR navigation and cellular dynamics modeling.

• YouTube
• 1 hour 10 minutes
• On-Demand
• Free Video