Machine Learning in Molecular Dynamics

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Page 2 of 4

Introduces feed-forward networks, covering neural network structure, training, activation functions, and optimization, with applications in forecasting and finance.

Unsupervised Learning: Clustering & Dimensionality Reduction

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.

Neural Networks: Two Layers Neural Network

Covers the basics of neural networks, focusing on the development from two layers neural networks to deep neural networks.

Neural Networks: Training and Optimization

Explores neural network training, optimization, and environmental considerations, with insights into PCA and K-means clustering.

Improving Density Predictions in Machine Learning

Explores enhancing machine learning predictions by refining error metrics and applying constraints for improved accuracy in electron density predictions.

Dimensionality Reduction: PCA and Autoencoders

Introduces artificial neural networks, CNNs, and dimensionality reduction using PCA and autoencoders.

Machine Learning for Feature Extraction

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

Understanding Machine Learning: Exactly Solvable Models

Explores machine learning through solvable models, covering sample complexity, neural networks, and computational gaps.

Stochastic numerical methods for many-body quantum systems

Explores stochastic methods for quantum systems, including exact diagonalization, variational methods, neural networks, and machine learning.

Replica computation and machine learning

Explores replica computation, algorithmic energy minimization, and their connection to machine learning concepts.