Lecture

Deep Neural Networks: Training and Optimization

Description

This lecture covers the training of deep neural networks, focusing on stochastic gradient descent, mini-batch processing, and normalization techniques. It also discusses strategies to prevent overfitting, such as dropout and L1/L2 regularization. Additionally, it explores the challenges of vanishing gradients and introduces residual networks. The presentation concludes with a look at Hebbian learning, recurrent neural networks, and different types of neural network architectures.

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.

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.