In the previous chapter, we learned various strategies to guide AI models 'down the mountain' (optimization algorithms), such ...

This paper presents a novel adaptive learning-rate backpropagation neural network (ALR-BPNN) algorithm based on the minimization of mean-square deviation (MSD) to implement a fast convergence rate and ...

This deep dive covers the full mathematical derivation of softmax gradients for multi-class classification. #Backpropagation #Softmax #NeuralNetworkMath #MachineLearning #DeepLearning #MLTutorial ...

A new technical paper titled “The backpropagation algorithm implemented on spiking neuromorphic hardware” was published by University of Zurich, ETH Zurich, Los Alamos National Laboratory, Royal ...

GitHub is where people build software. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects.

A general backpropagation algorithm is proposed for feedforward neural network learning with time varying inputs. The Lyapunov function approach is used to rigo ...

The Forward-Forward algorithm (FF) is comparable in speed to backpropagation but has the advantage that it can be used when the precise details of the forward computation are unknown.

In this regard, Hinton proposes the FF algorithm as an alternative to backpropagation for neural network learning. The FF algorithm is inspired by Boltzmann machines (Hinton and Sejnowski, 1986) and ...

The backpropagation algorithm computes the gradient of the loss function with respect to the weights. these algorithms are complex and visualizing backpropagation algorithms can help us in ...