(Old) Lecture 2 | The Universal Approximation Theorem

Neural Networks & the Universal Approximation Theorem

Universal Approximation Theorem

Function approximation by using neural network. (Machine learning, Deep learning)

How can a deep neural network approximate a continuous function over a compact set (UAT)?

Lecture 8: Non-Linear Feature Engineering (Foundation for Universal Approximation Theorem)

L24 Reinforcement Learning (4) - Actor-Critic and Deep RL - Algorithms in Machine Learning

Deep RL

Seminar 3: On the approximation capabilities of neural networks

Lecture 5: Neural Networks

Mathematics Colloquium: Deep learning, inference and inverse problems | Maarten V. de Hoop

Q-NET: A Network for Low-Dimensional Integrals of Neural Proxies

Is Distance Matrix Enough for Geometric Deep Learning? | Zian Li

A Corrective View of Neural Networks: Representation, Memorization and Learning

Machine Learning Lecture 35 "Neural Networks / Deep Learning" -Cornell CS4780 SP17

RL Course by David Silver - Lecture 6: Value Function Approximation

Gail Weiss: Thinking Like Transformers

Neural Network - function approximation with regularization

Lecture20 - Part 2 - Universal Approximation

01L – Gradient descent and the backpropagation algorithm