Machine-learned force fields for molecular simulation

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Machine Learning and Modern AI: SWOT Analysis

Covers a SWOT analysis of Machine Learning and Artificial Intelligence, exploring strengths, weaknesses, opportunities, and threats in the field.

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.

Gradient-Based Algorithms in High-Dimensional Learning

Provides insights on gradient-based algorithms, deep learning mysteries, and the challenges of non-convex problems.

Quantum Chemistry

Covers eigenvalues, eigenfunctions, Hermitian operators, and the measurement of observables in quantum chemistry.

Introduction to Supervised Learning

Introduces supervised learning using labeled data points to optimize classifier output.

Classical and Quantum Mechanics: Foundations and Applications

Explores classical and quantum mechanics, covering observables, momentum, Hamiltonian, and the Schrödinger equation, as well as quantum chemistry and the Schrödinger's cat experiment.

Neural Networks: Supervised Learning and Backpropagation

Explains neural networks, supervised learning, and backpropagation for training and improving performance.

Variational Principle in Quantum Mechanics

Delves into the variational principle in quantum mechanics and the application of deep neural networks for complex system solutions.

Self-Supervised Image Representation Learning

Presents BYOL, a self-supervised image representation learning method achieving state-of-the-art results without negative pairs.

Machine Learning at the Atomic Scale

Explores simple models, electronic structure evaluation, and machine learning at the atomic scale.