Mathematics of Neural Networks (Lecture Notes Graduate Course)
2024年3月6日 · It consists of two parts: first a general introduction to deep learning that focuses on introducing the field in a formal mathematical way. The second part provides an introduction to the theory of Lie groups and homogeneous spaces and how it can be applied to design neural networks with desirable geometric equivariances.
In this work, we are going to introduce Neural Networks. First, we are going to give a mathematical formulation of the concept of Neural Networks. Later on, we will examine some important properties of Neural Networks and make a connection to common statistical methods such as Principal Component Analysis and Singular Value Decomposition.
Math6380p: Deep Learning
The aim of this course is to provide graduate students who are interested in deep learning a variety of understandings on neural networks that are currently available to foster future research.
2011年11月1日 · ural networks overlap several bodies of mathematics. Neural network goals, such as classifica ion, regression and clustering come from statistics. The gradient descent that goes into backpropagation, and ot
The Complete Mathematics of Neural Networks and Deep …
2021年2月28日 · A complete guide to the mathematics behind neural networks and backpropagation. In this lecture, I aim to explain the mathematical phenomena, a combination of linear algebr...more
MATH 6373 Deep Learning and Neural Networks - math.uh.edu
The lectures will cover basic algorithmic principles underlying automatic learning, implementation and properties of deep learning architectures and selected applications using real data.
The primary set-up for learning neural networks is to define a cost function (also known as a loss function) that measures how well the network predicts outputs on the test set.
Neural Networks - Free Computer, Programming, Mathematics, …
This book provides a clear and detailed coverage of fundamental neural network architectures and learning rules. It emphasizes a coherent presentation of the principal neural networks, methods for training them and their applications to practical problems. Neural Network Learning: Theoretical Foundations
The Complete Mathematics of Neural Networks and Deep …
2024年9月7日 · A complete guide to the mathematics behind neural networks and backpropagation. In this lecture, I aim to explain the mathematical phenomena, a combination of linear algebra and optimization, that underlie the most important algorithm in data science today: the feed forward neural network.
Understanding the stakes of these methods raises questions at the interfaces between mathematics and algorithmics. In this article, I will explain the structure of these networks as well as the key concepts of their supervised learning. Neural networks are algorithms, which compute, from an input x (for example an image), an output y.