YoVDO

Mathematics for Deep Neural Networks: Introduction - Lecture 1 of 5

Offered By: Georgia Tech Research via YouTube

Tags

Deep Learning Courses Mathematics Courses Artificial Intelligence Courses Data Science Courses Machine Learning Courses Neural Networks Courses Algorithms Courses Statistical Learning Theory Courses

Course Description

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore the foundations of deep neural networks in this comprehensive lecture, part of the TRIAD Distinguished Lecture Series. Delve into the mathematics behind various neural network types, examining their complexity and data processing capabilities. Gain insights into the algorithms used for fitting deep networks to data and survey the key ideas underlying existing mathematical theories. Discover the diverse approaches to understanding deep networks through a mathematical lens, providing a solid foundation for further exploration in the field of artificial intelligence and machine learning.

Syllabus

TRIAD Distinguished Lecture Series | Johannes Schmidt-Hieber Lecture 1 (of 5)


Taught by

Georgia Tech Research

Related Courses

Neural Networks for Machine Learning
University of Toronto via Coursera Good Brain, Bad Brain: Basics
University of Birmingham via FutureLearn Statistical Learning with R
Stanford University via edX Machine Learning 1—Supervised Learning
Brown University via Udacity Fundamentals of Neuroscience, Part 2: Neurons and Networks
Harvard University via edX