Deep Learning with Yacine on MSN

20 Activation Functions in Python for Deep Neural Networks – ELU, ReLU, Leaky-ReLU, Sigmoid, Cosine

Explore 20 different activation functions for deep neural networks, with Python examples including ELU, ReLU, Leaky-ReLU, ...
Frontiers

Spiking Neural Networks at Scale: Learning Algorithms, Architectures, and Real-World Applications

Spiking Neural Networks (SNNs) are a cutting-edge approach to artificial intelligence, designed to emulate the brain's architecture and functionality. Their ...
IEEE

Enhancing Satellite Image Classification in Remote Sensing Applications Utilizing Deep Convolutional Neural Networks

Abstract: Satellite image classification is a key task in remote sensing, where deep learning models are increasingly applied for their accuracy and automation capabilities. This study conducts a ...
Analytics Insight

NumPy vs. PyTorch: What’s Best for Your Numerical Computation Needs?

Overview: NumPy is ideal for data analysis, scientific computing, and basic ML tasks.PyTorch excels in deep learning, GPU ...
Deep Learning with Yacine on MSN

Deep Neural Network from Scratch in Python – Fully Connected Feedforward Tutorial

Learn how to build a fully connected, feedforward deep neural network from scratch in Python! This tutorial covers the theory ...
techxplore

Mice and AI neural networks reveal similar patterns when learning to cooperate

At a time when conflict and division dominate the headlines, a new study from UCLA finds remarkable similarities in how mice and artificial intelligence systems each develop cooperation: working ...
eLife

Neural dynamics of reversal learning in the prefrontal cortex and recurrent neural networks

The findings of this study are valuable, offering insights into the neural representation of reversal probability in decision-making tasks, with potential implications for understanding flexible ...
IEEE

Deep Graph Wavelet Neural Networks Using Heat Kernel for Semi-supervised Node Classification

Abstract: Graph convolutional neural networks have demonstrated promising solutions for processing non-Euclidean data in tasks such as node classification. While existing graph convolution models aim ...