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, ...
InfoWorld

7 newer data science tools you should be using with Python

Already using NumPy, Pandas, and Scikit-learn? Here are seven more powerful data wrangling tools that deserve a place in your ...
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 ...
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 ...
IEEE

MAML-KalmanNet: A Neural Network-Assisted Kalman Filter Based on Model-Agnostic Meta-Learning

Abstract: Neural network-assisted (NNA) Kalman filters provide an effective solution to addressing the filtering issues involving partially unknown system information by incorporating neural networks ...

From Raw Data to Insights in Minutes : Kumo AI Data Science Service

The platform that makes advanced data science accessible with Graph Neural Networks and Predictive Query Language.
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 ...
C&EN

ML-PLA: Enhancing Protein–Ligand Binding Affinity Prediction with Microenvironment and Long-Range Interaction-Aware Graph Neural Networks

School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan, Hubei 430200, China ...
C&EN

A Multi-Stage Graph Neural Network–Physics-Informed Neural Network (GNN–PINN) Framework for Thermodynamic Property Prediction

Department of Chemistry and Chemical Engineering, Education and Research Center for Smart Energy and Materials, Inha University, Incheon 22212, Republic of Korea ...
IEEE

Adaptive Gradient Neural Networks for Solving the Time-Varying Sylvester Equation

Abstract: This paper develops several new dynamical designs, based on the gradient neural network (GNN), from the perspective of control theory to solve the time-varying Sylvester equation (TVSE). We ...