Lecture
Bagging: Regularization Method in Deep Learning
Related lectures (62)
Deep Learning for Autonomous Vehicles: Learning
Explores learning in deep learning for autonomous vehicles, covering predictive models, RNN, ImageNet, and transfer learning.
AI Gamer: D4
Explores reinforcement learning in AI to master games using neural networks.
Inductive Bias in Machine Learning
Explores the concept of inductive bias in machine learning, emphasizing the role of prior knowledge in designing effective neural networks.
Neuroscience Inspired Artificial Intelligence
Explores the historical development of deep learning, reinforcement learning, attention mechanisms, and memory systems in AI inspired by neuroscience.
Improving Density Predictions in Machine Learning
Explores enhancing machine learning predictions by refining error metrics and applying constraints for improved accuracy in electron density predictions.
Introduction to policy gradientMOOC: Neuro Robotics
Introduces policy gradients, optimizing rewards by associating actions with observations.
Modeling the input space
Explores modeling continuous input spaces in reinforcement learning using neural networks and radial basis functions.
Machine Learning on Earth System with Remote Sensing
Explores machine learning applications in Earth system analysis using remote sensing data, focusing on automatic image interpretation and explainable AI.
Policy Gradient Methods: Single Neuron Example
Covers policy gradient methods using a single neuron with binary output.
Filter and Graph Learning with Optimal Transport
Introduces FIGLearn, a method for learning filters and graphs using optimal transport, outperforming current state-of-the-art.