In this article, I'll explain how to implement the back-propagation (sometimes spelled as one word without the hyphen) neural network training algorithm from scratch, using just Python 3.x and the ...

Learn how forward propagation works in neural networks using Python! This tutorial explains the process of passing inputs ...

Learn how backpropagation works using automatic differentiation in Python. Step-by-step implementation from scratch.

Neural networks made from photonic chips can be trained using on-chip backpropagation – the most widely used approach to training neural networks, according to a new study. The findings pave the way ...

A new technical paper titled “Hardware implementation of backpropagation using progressive gradient descent for in situ training of multilayer neural networks” was published by researchers at ...

While it has become indispensable for the success of DNNs, BP has several limitations, such as slow convergence, overfitting, high computational requirements, and its black box nature. Recently, ...

In this work, a gradient method with momentum for BP neural networks is considered. The momentum coefficient is chosen in an adaptive manner to accelerate and stabilize the learning procedure of the ...

Over the past year or so, among my colleagues, the use of sophisticated machine learning (ML) libraries, such as Microsoft's CNTK and Google's TensorFlow, has increased greatly. Most of the popular ML ...