Overview: NumPy is ideal for data analysis, scientific computing, and basic ML tasks.PyTorch excels in deep learning, GPU computing, and automatic gradients.Com ...

Predicting tropical cyclones (TCs) accurately is crucial for disaster mitigation and public safety. Although the forecasting accuracy of TC tracks has improved substantially in recent decades, ...

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

Overview A mix of beginner and advanced-level books to suit various learning needs.Each book blends theory with practical ...

Artificial intelligence has taken many forms over the years and is still evolving. Will machines soon surpass human knowledge ...

In today's data-driven environment, Python has become the mainstream language in the fields of machine learning and data science due to its concise syntax, rich library support, and active community, ...

1 Institute of Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia 2 Department of Learning, Data Analytics and Technology, Section Cognition, Data and ...

Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and other artificial intelligence (AI) models, learn to make accurate ...

This study proposes a hybrid modeling approach that integrates a Physics Informed Neural Network (PINN) and a long short-term memory (LSTM) network to predict river water temperature in a defined ...

What is this book about? Graph neural networks are a highly effective tool for analyzing data that can be represented as a graph, such as social networks, chemical compounds, or transportation ...

Abstract: Accurate classification of seismic events is essential to prevent confusion in catalogs, which can distort seismic statistics and hazard analyses. Monitoring small-scale explosions aids in ...