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 …

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.

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 …

I'm reading the Stanford course about Convolutional Neural Network and I don't understand how he backpropagates a 2 neural network. Actually, the thing I'm trying to ...

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 …

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...

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 …

Sep 6, 2023 · 2 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 …

Apr 29, 2019 · To get started on learning about convolutional neural network and other more complicated structures, Wikipedia is a good resource

I'm trying to follow a blog post about Graph Convolutional Neural Networks. To set up some notation, the above blog post denotes a graph $\mathcal {G}$, it's adjacency matrix $A$, and the degree matrix $D$.