Skip to main content
Graph
Search
fr
|
en
Switch to dark mode
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Practice
Publications
Startups
Units
Show all results for
Home
Lecture
Machine Learning for Solving PDEs: Random Feature Method
Graph Chatbot
Related lectures (32)
Previous
Page 2 of 4
Next
Machine Learning for Feature Extraction
Explores machine learning for feature extraction, 3D vision, and neural networks in mobile robotics.
Neural Networks: Two Layers Neural Network
Covers the basics of neural networks, focusing on the development from two layers neural networks to deep neural networks.
Neural Networks: Multilayer Learning
Covers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.
Neural Network Approximation and Learning
Delves into neural network approximation, supervised learning, challenges in high-dimensional learning, and deep learning experimental revolution.
Understanding Deep Learning
Explores deep learning fundamentals, including image classification, neural network working principles, and machine learning challenges.
Statistical Physics in Machine Learning: Understanding Deep Learning
Explores the application of statistical physics in understanding deep learning with a focus on neural networks and machine learning challenges.
Deep Learning: Principles and Applications
Covers the fundamentals of deep learning, including data, architecture, and ethical considerations in model deployment.
High Order PDE-Convergence: ADI-Type Integrators
Covers the convergence of high-order PDEs using ADI-type integrators for parabolic problems.
Neural Networks: Training and Activation
Explores neural networks, activation functions, backpropagation, and PyTorch implementation.
Untitled