Generative Models Courses and Certifications

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

DeepLearning.AI;  Amazon Web Services

Generative AI with Large Language Models

3619 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
• Self-Paced
• Paid Course

Career Certificate

Business Statistics and Analysis

12721 ratings at Coursera

Equip yourself with essential business data analysis tools, from spreadsheet functions to data modeling, probability concepts, and linear regression, culminating in a practical capstone project.

• Coursera
• 12 weeks, 10 hours/week
• Self-Paced
• Paid Course
• 5 courses

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
• Self-Paced
• 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
• Self-Paced
• Free Video

Center for Language & Speech Processing(CLSP), JHU

Flexible Models for Microclustering with Application to Entity Resolution - 2016

Explore microclustering models for entity resolution and community detection, focusing on sublinear cluster growth and applications in various fields like genetics and human rights.

• YouTube
• 1 hour 10 minutes
• Self-Paced
• Free Video

Broad Institute

PROTPARDELLE: An All-Atom Protein Generative Model - Broad Institute Machine Learning in Drug Discovery Symposium 2023

Explore PROTPARDELLE, an all-atom protein generative model, presented by Stanford's Possu Huang at the Broad Institute's Machine Learning in Drug Discovery Symposium.

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

Valence Labs

Protein Discovery with Discrete Walk-Jump Sampling

Explore discrete generative modeling for protein discovery using Walk-Jump Sampling. Learn to train energy functions, sample with Langevin MCMC, and optimize for distributional conformity in antibody design.

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

Simons Institute

Towards Understanding Generalization Properties of Score-Based Losses

Explore score-based losses as alternatives to maximum likelihood in generative models, examining their statistical efficiency and connections to diffusion algorithms for improved model design.

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

Deep Generative Models - Autoregressive Models - Lecture 3

Explore autoregressive models in deep learning, focusing on their architecture, training, and applications in generating sequential data like text and images.

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

Deep Generative Models - Lecture 10: GANs

Explore Generative Adversarial Networks (GANs) in deep learning, covering their architecture, training process, and applications in image generation and beyond.

• YouTube
• 1 hour 28 minutes
• Self-Paced
• Free Video