Neural Network Quantum States

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

Explores quantum mechanics, emphasizing the harmonic oscillator and particle confinement in a box.

Covers the fundamentals of quantum mechanics and the behavior of particles at the quantum level.

Explores the Random Feature Method for solving PDEs using machine learning algorithms to approximate high-dimensional functions efficiently.

Explores neural quantum states and their representation using artificial neural networks.

Offers insights into the statistical physics of learning, exploring the relationship between neural network structure and disordered systems.

Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.

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

Explores learning from interconnected data using graphs, covering challenges, GNN design, research landscapes, and democratization of Graph ML.

Covers data representations and processing techniques essential for effective machine learning algorithms.