Generative Models: Advancements in Molecular Design

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Related lectures (27)

Active Learning for Molecular Design

Covers machine learning approaches for material design, practical examples, and software tools for research.

Reinforcement Learning: Policy Gradient and Actor-Critic Methods

Provides an overview of reinforcement learning, focusing on policy gradient and actor-critic methods for deep artificial neural networks.

Dynamics of Linear Neural Networks

Explores the learning dynamics of deep neural networks using linear networks for analysis, covering two-layer and multi-layer networks, self-supervised learning, and benefits of decoupled initialization.

Machine Learning Fundamentals

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.

Computational Molecular Design

Explores Computational Molecular Design, focusing on Mathematical Theory, High Performance Computing, and In Vivo Experiments, with an emphasis on quantum chemistry and electron dynamics.

Structures in Non-Convex Optimization

Covers non-convex optimization, deep learning training problems, stochastic gradient descent, adaptive methods, and neural network architectures.

Machine Learning for Feature Extraction

Explores machine learning for feature extraction, 3D vision, and neural networks in mobile robotics.

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