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Neural network Python from scratch with softmax

Implement Neural Network in Python from Scratch ! In this video, we will implement MultClass Classification with Softmax by making a Neural Network in Python from Scratch. We will not use any build in ...
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 ...
InfoWorld

PythoC: A new way to generate C code from Python

PythoC lets you use Python as a C code generator, but with more features and flexibility than Cython provides. Here’s a first look at the new C code generator for Python. Python and C share more than ...
Yahoo

Cyber giant F5 Networks says government hackers had ‘long-term’ access to its systems, stole code and customer data

Add Yahoo as a preferred source to see more of our stories on Google. Cybersecurity firm F5 Networks says government-backed hackers had “long-term, persistent access” to its network, which allowed ...
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 ...
The Journal

Network to Code Intros AI Assistant for Enterprise Network Teams

Network automation firm Network to Code has introduced NautobotGPT, an AI-powered assistant aimed at helping enterprise network engineers create, test, and troubleshoot automation tasks more ...
Phys.org

Self-learning neural network cracks iconic black holes

A team of astronomers led by Michael Janssen (Radboud University, The Netherlands) has trained a neural network with millions of synthetic black hole data sets. Based on the network and data from the ...
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 ...