Lecture
Statistical Learning Theory: Conclusions on Deep Learning
In course
DEMO: pariatur aliqua velit reprehenderit
Irure reprehenderit anim laborum do ipsum mollit. Velit labore laboris ullamco ea. Anim dolor cupidatat reprehenderit laboris ex ipsum enim ipsum mollit deserunt exercitation. Officia Lorem elit exercitation qui aute. Amet anim commodo incididunt cupidatat id ex aute dolore adipisicing ea dolore veniam deserunt in. Aliquip ut amet ullamco officia deserunt nulla fugiat in. Elit sunt aliquip culpa consectetur deserunt.
Description
This lecture covers the conclusions on deep learning, focusing on the maximization of neuron activity in different layers and categories using images, as well as an introduction to statistical learning theory with topics like Huelding bound and loss functions.
Instructor
elit eiusmod
Laboris ea do amet id ullamco magna. Sit commodo aute non nisi occaecat fugiat qui cillum ad. Lorem consectetur ut reprehenderit sit occaecat consequat reprehenderit quis qui aute. Tempor duis minim eiusmod enim aliqua sint anim aliquip occaecat laboris laborum non. Elit anim fugiat nisi sunt proident et anim. Duis officia Lorem non eu.
Official source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Ontological neighbourhood
Related lectures (107)
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.
Perception: Data-Driven Approaches
Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.
Neural Networks: Training and Activation
Explores neural networks, activation functions, backpropagation, and PyTorch implementation.
Neural Networks: Deep Neural Networks
Explores the basics of neural networks, with a focus on deep neural networks and their architecture and training.
Introduction to Machine Learning
Provides an overview of Machine Learning, including historical context, key tasks, and real-world applications.