Mar 13, 2019 · I am looking for a book that goes through the mathematical aspects of neural networks, from simple forward passage of multilayer perceptron in matrix form or differentiation of activation …

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 …

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. …

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 $D$.

I was reading Yoshua's Bengio [book] [1] on convolutional neural networks and it has small section that described/explains the convolution in the context of estimating the location of a spaceship with a …

Jan 27, 2017 · Among many uses and applications of the logistic function/hyperbolic tangent there are: Being an activation function for Neural Networks. These are universal function approximators that …

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 …

Mar 27, 2018 · In your neural networks application, the optimisation algorithm always wants to go in the direction where the cost decreases the most. The algorithm doesn't see the cost globally, only in …

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 ...

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 …