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.

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...

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 …

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 $D$.

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 …

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 …

Jun 2, 2019 · The analogy to this in classical convolutional neural networks is simply the Euclidean image patch around a given point, which is convolved with a kernel to give rise to the new feature …

May 28, 2011 · In case you were under a rock, from 2012 on onwards deep learning algorithms have quickly become the best known algorithms for a variety of problems, including notably image …