What are the best books to study Neural Networks from a purely ...
Mar 13, 2019 · 2 One of my favorite books on theoretical aspects of neural networks is Anthony and Bartlett's book: "Neural Network Learning Theoretical Foundations". This book studies neural …
graph theory - Convolutional neural networks preserve local ...
Oct 13, 2020 · I'm working on a project about graph neural networks and was reading some stuff about convolutional neural networks. I did not understand what the book means with 'preserve local …
neural networks - How does the reshape works in im2col for CNN's ...
Aug 9, 2025 · I'm implementing a Convolutional Neural Network and im2col optimization from scratch (without deep learning libraries), and I got stuck when computing the backpropagation for the kernel.
Area of intersection between two circles - Mathematics Stack Exchange
Suppose you have 2 circles that intersect each other in such a way that each circle passes through the other's center. What is the area between the circle(or common area) i.e. area between the cent...
How many parameters does the neural network have?
Aug 26, 2019 · We have a neural network with an input layer of ℎ0 nodes, hidden layers of ℎ1 , ℎ2 , ℎ3 , ..., ℎ𝑙−1 nodes respectively and an output layer of ℎ𝑙 nodes. How many parameters does the network …
derivative of cost function for Neural Network classifier
Jun 15, 2017 · derivative of cost function for Neural Network classifier Ask Question Asked 8 years, 7 months ago Modified 2 years, 5 months ago
Neural Network topology - Mathematics Stack Exchange
Apr 29, 2019 · To get started on learning about convolutional neural network and other more complicated structures, Wikipedia is a good resource
Derivation of back-propagation equation $\frac {\partial E (\theta ...
Jul 15, 2015 · I was reading the following paper on convolution stacked auto-encoders and they had the following convolution neural network (for auto-encoders, notice I didn't write the offset term [to avoid …
How can an algorithm for traveling salesman beat concorde?
Sep 6, 2023 · I am trying to learn about neural networks. I was reading the paper An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem which uses graph neural …
Simply put, are most functions in the "real world" non-convex?
Jan 16, 2022 · Below are some visualizations of the "loss functions" from a Convolutional Neural Network (CNN), used in image recognition: I have heard people make such claims, such as the "loss …