Lecture
Scientific Machine Learning: Applications and Algorithms
Related lectures (32)
Neural Networks: Perceptron Model and Backpropagation Algorithm
Covers the perceptron model and backpropagation algorithm in neural networks.
Group Actions: Theory and Examples
Explores concrete examples of group actions on sets, focusing on actions that do not change the set.
Fully Connected Networks on MNIST and SUSY Datasets
Covers the implementation of fully connected neural networks on two datasets using PyTorch.
Neural Networks
Explores neural networks, hidden layers, weight adjustments, activation functions, and the universal approximation theorem.
Introduction to Machine Learning
Provides an overview of Machine Learning, including historical context, key tasks, and real-world applications.
Document Analysis: Topic Modeling
Explores document analysis, topic modeling, and generative models for data generation in machine learning.
Introduction to Machine Learning
Covers the basics of machine learning, including supervised and unsupervised learning, linear regression, and classification.
Deep Learning for Autonomous Vehicles: Predictive Models
Explores predictive models and trackers for autonomous vehicles, covering object detection, tracking challenges, neural network-based tracking, and 3D pedestrian localization.
Neural Networks: Training and Activation
Explores neural networks, activation functions, backpropagation, and PyTorch implementation.
Self-supervised Learning for Autonomous Vehicles
Explores self-supervised learning for autonomous vehicles, deriving labels from data itself and discussing its applications and challenges.