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 …

Sep 13, 2019 · I'm currently studying deep learning with the book Deep Learning (Goodfellow et al., 2015) and had a question regarding the convolution operation of convolutional 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 …

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 …

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 …

Jul 9, 2020 · There are many examples of neural networks for MNIST hand-written digits classification problem, where the output is a 10-element softmax-vector with one maximum …

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 …

Jul 10, 2015 · This seems to be common in convolutional neural networks for example and its confusing me. I am trying to come up with an example to make this concrete and not confusing.

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 …

May 11, 2020 · An understanding of linear transformations can help better understand these processes. These can range from very simplistic things like scaling a recipe to more complex …