Every Python developer knows some or all of these libraries, because they’re stable, reliable, and excellent at what they do.

Physics-aware machine learning integrates domain-specific physical knowledge into machine learning models, leading to the development of physics-informed neural networks (PINNs). PINNs embed physical ...

NeuralCoref is a pipeline extension for spaCy 2.1+ which annotates and resolves coreference clusters using a neural network. NeuralCoref is production-ready, integrated in spaCy's NLP pipeline and ...

There’s a lot to know about search intent, from using deep learning to infer search intent by classifying text and breaking down SERP titles using Natural Language Processing (NLP) techniques, to ...

Training neural networks to perform different tasks is relevant across various disciplines. In particular, Recurrent Neural Networks (RNNs) are of great interest in Computational Neuroscience.

Data Science expert with desire to help companies advance by applying AI for process improvements. This publication provides an in-depth overview of various neural network layers, including their ...

Machine learning with neural networks is sometimes said to be part art and part science. Dr. James McCaffrey of Microsoft Research teaches both with a full-code, step-by-step tutorial. A binary ...

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 ...