Neural Networks and Quantum Field Theory

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Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.

Covers the fundamentals of multi-layer neural networks and the training process of fully connected networks with hidden layers.

Explores the universal critical exponents of microscopic systems and the role of the renormalization group.

Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.

Explores the dynamical consequences of symmetry, emphasizing conservation laws and the role of Noether currents.

Covers the Dotsenko-Fateev approach to computing correlation functions in quantum field theory.

Covers unitarity bounds, operator product expansion, conformal blocks, and the conformal bootstrap in conformal field theory.

Explores symmetries in quantum field theory, focusing on transformation rules and Casimir operators.

Introduces discrete symmetries, operators, and tetraquarks in quantum field theory.

Delves into neural network approximation, supervised learning, challenges in high-dimensional learning, and deep learning experimental revolution.