Generative Media Courses and Certifications

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
• Self-Paced
• Free Video

Valence Labs

Bayesian Flow Networks: A New Class of Generative Models

Explore Bayesian Flow Networks, a novel generative model class. Delve into mathematical foundations, continuous and discrete data applications, and implementation details through pseudocode and Q&A.

• YouTube
• 1 hour 27 minutes
• Self-Paced
• Free Video

Valence Labs

Equivariant Flow Matching for Deep Generative Models in Physics

Explore equivariant flow matching for efficient, simulation-free training of equivariant CNFs in physics and drug discovery, improving sampling efficiency and scalability for many-particle systems.

• YouTube
• 1 hour 8 minutes
• Self-Paced
• 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
• Self-Paced
• Free Video

Valence Labs

PFGM++: Unlocking the Potential of Physics-Inspired Generative Models

Explore PFGM++, a physics-inspired generative model unifying diffusion models and PFGMs. Learn about its flexible embedding, unbiased objectives, and superior performance on image datasets.

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

Valence Labs

Improving and Generalizing Flow-Based Generative Models with Minibatch Optimal Transport

Explore advanced techniques for improving continuous normalizing flows, including generalized conditional flow matching and optimal transport CFM, enhancing generative modeling for various applications.

• YouTube
• 1 hour 17 minutes
• Self-Paced
• Free Video

Valence Labs

GenPhys: From Physical Processes to Generative Models

Explore how physical processes inspire new generative models, including diffusion and Poisson flow. Learn to translate PDEs into AI algorithms and expand the design space of generative approaches.

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

Valence Labs

An Optimal Control Perspective on Diffusion-Based Generative Modeling

Explore optimal control theory's connection to diffusion-based generative modeling. Gain insights into stochastic differential equations, Hamilton-Jacobi-Bellman equations, and novel sampling methods for unnormalized densities.

• YouTube
• 1 hour 11 minutes
• Self-Paced
• Free Video

Valence Labs

Score Matching via Differentiable Physics

Explore score matching via differentiable physics for inverse problems. Learn about combining physics operators with stochastic differential equations to generate plausible trajectories towards given end states.

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

Valence Labs

D-Flow: Differentiating through Flows for Controlled Generation

Explore D-Flow framework for controlled generation in AI models, optimizing source points through flow differentiation. Learn applications in inverse problems and conditional generation.

• YouTube
• 1 hour 3 minutes
• Self-Paced
• Free Video

Valence Labs

Generalization in Diffusion Models from Geometry-Adaptive Harmonic Representation

Explore generalization in diffusion models through geometry-adaptive harmonic representation. Analyze denoising DNNs, score functions, and inductive biases to understand high-dimensional density learning and optimal basis adaptation.

• YouTube
• 1 hour 35 minutes
• Self-Paced
• Free Video

Outlier

Flow Matching - Intuitive Explanation and PyTorch Implementation

Dive into Flow Matching's simplified approach to diffusion models through intuitive explanations, mathematical derivations, and hands-on PyTorch implementation for advanced AI image generation.

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

Data Science Courses

Denoising Diffusion Probabilistic Models and Generative Moment Matching Networks - Lecture 17

Delve into diffusion models and DDPMs, exploring progressive data refinement techniques and generative moment matching networks using Maximum Mean Discrepancy for distribution analysis.

• YouTube
• 1 hour 9 minutes
• Self-Paced
• Free Video

Outlier

Diffusion Models and Score-Based Generative Models Explained - From Scratch

Explore score-based generative models and their connection to diffusion models. Gain intuition on DDPM, DDIM, and EDM through mathematical explanations and visualizations.

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

MattVidPro AI

Flux Context vs GPT-4 - Advanced AI Image Generation and Editing Comparison

Explore Flux Context's advanced image generation capabilities through hands-on demos, comparing it to GPT-4 for object removal, background changes, and character creation tasks.

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