Lecture

Convolutional Neural Networks

Description

This lecture covers the fundamentals of Convolutional Neural Networks (CNNs), explaining the structure and function of convolutional layers, pooling layers, and fully connected layers. It also delves into the concept of 2x2 filters, the process of max and average pooling, and the importance of weight sharing in CNNs.

Instructor

magna dolor

Mollit laborum reprehenderit anim exercitation velit incididunt duis culpa aliqua. Fugiat Lorem sunt commodo aliquip aute velit laborum ad. Do est Lorem Lorem excepteur minim. In in enim pariatur qui aliqua eu dolor ad id. Cillum in laboris enim et minim culpa. Irure laborum esse adipisicing tempor voluptate consectetur adipisicing cillum proident quis cupidatat dolore. Cillum tempor incididunt sit aliquip.

Official source

https://mediaspace.epfl.ch/media/0_mz0ex72r

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

Computer engineering

Computer networks: Local area networks, Internet protocol suite

Information engineering

Signal processing: Digital image processing

Machine learning: Artificial neural networks

Biology

Cell biology: Neurotransmission

Related lectures (89)

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.