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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.
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.
| Erscheinungsjahr: | 2025 |
|---|---|
| Fachbereich: | Allgemeines |
| Genre: | Biologie, Importe |
| Rubrik: | Naturwissenschaften & Technik |
| Thema: | Lexika |
| Medium: | Taschenbuch |
| Inhalt: | Einband - flex.(Paperback) |
| ISBN-13: | 9781098168032 |
| ISBN-10: | 1098168038 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: |
Ravarani, Charles
Latysheva, Natasha |
| Hersteller: | O'Reilly Media |
| Verantwortliche Person für die EU: | Libri GmbH, Europaallee 1, D-36244 Bad Hersfeld, gpsr@libri.de |
| Maße: | 232 x 174 x 23 mm |
| Von/Mit: | Charles Ravarani (u. a.) |
| Erscheinungsdatum: | 01.08.2025 |
| Gewicht: | 0,75 kg |
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