Computer Weekly

PyTorch Foundation lights up welcomes for Helion & Safetensors

The PyTorch Foundation also welcomed Safetensors as a PyTorch Foundation-hosted project. Developed and maintained by Hugging ...
IEEE

Neural Network Models for Cross-Language Code Synthesis and Translation

Abstract: Code translation between programming languages is a complex task that poses significant challenges in maintaining both the structure and functionality of the translated code. This work ...
Hosted on MSN

Build a deep neural network from scratch in Python

We will create a Deep Neural Network python from scratch. We are not going to use Tensorflow or any built-in model to write the code, but it's entirely from scratch in python. We will code Deep Neural ...
IEEE

A Neural Network-Based ADC Calibration Framework via Quantization Code Reconstruction

Abstract: This article proposes a neural network (NN)-based calibration framework via quantization code reconstruction to address the critical limitation of multidimensional NNs (MDNNs) in ...
Network World

HPE OneView vulnerable to remote code execution attack

An unauthenticated user can execute the attack, and there’s no mitigation, just a hotfix that should be applied immediately. A maximum severity remote code execution vulnerability in Hewlett Packard ...
GitHub

Source code for a neural network-based framework for signal separation in spatio-temporal gravity data

B. Heller-Kaikov, R. Pail and M. Werner, Neural network-based framework for signal separation in spatio-temporal gravity data. Computers and Geosciences (2025), doi ...
Hosted on MSN

Dropout In Neural Networks — Prevent Overfitting Like A Pro (With Python)

This video is an overall package to understand Dropout in Neural Network and then implement it in Python from scratch. Dropout in Neural Network is a regularization technique in Deep Learning to ...
Frontiers

NESTML: a generic modeling language and code generation tool for the simulation of spiking neural networks with advanced plasticity rules

With increasing model complexity, models are typically re-used and evolved rather than starting from scratch. There is also a growing challenge in ensuring that these models can seamlessly work across ...