Neural Networks and Quantum Field Theory

Related lectures (30)

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

Covers the connection between statistical models and unitary conformal field theories, focusing on critical points and the role of local fields.

Covers the Easing model conformal theory, focusing on scaling limits and correlation functions.

Covers the basics of Conformal Field Theory, focusing on Large N CFTs and correlation functions.

Covers cross section, lifetime, quantum fluid, asymptotic states, discrete symmetries, and normal ordering in quantum field theory.

Covers local fields and correlations in Ising models and conformal field theory.

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

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

Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.

Explores weakly coupled quantum field theory in AdS, including state-operator correspondence and semiclassical gravity.