Deep Learning for Biology

Harness AI to Solve Real-World Biology Problems

von Charles Ravarani, Natasha Latysheva

300 Seiten, Taschenbuch

Kurzbeschreibung des Verlags

Bridge the gap between modern machine learning and real-world biology with this practical, project-driven guide. Whether your background is in biology, software engineering, or data science, Deep Learning for Biology gives you the tools to develop deep learning models for tackling a wide range of biological problems. Authors Charles Ravarani and Natasha Latysheva guide you through hands-on projects applying deep learning to domains like DNA, proteins, biological networks, medical images, and microscopy. Each chapter is a self-contained mini-project, with step-by-step explanations that teach you how to train and interpret deep learning models using real biological data. - Build models for real-world biological problems such as gene regulation, protein function prediction, drug interactions, and cancer detection - Apply architectures like convolutional neural networks, transformers, graph neural networks, and autoencoders - Use Python and interactive notebooks for hands-on learning - Build problem-solving intuition that generalizes beyond biology Whether you're exploring new methods, transitioning into computational biology, or looking to make sense of machine learning in your field, this book offers a clear and approachable path forward.

• Themen: Informatik und Informationstechnologie Informatik Künstliche Intelligenz (KI) Maschinelles Lernen
• ISBN: 9781098168032
• Sprache: Englisch
• Erscheinungsdatum: 01.08.2025
• Größe: 232 x 174 mm
• Verlag: O'Reilly Media