Deep Learning for Biology
Language: English
Published by O'reilly Media Aug 2025, 2025
- Softcover
- New
Seller: Wegmann1855, Zwiesel, GermanyWegmann1855
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Add to basketItem description from seller
Neuware -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 not Elektronisches Buch 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.…
Seller Inventory # 9781098168032
- Title
- Deep Learning for Biology
- Author
- Charles Ravarani
- Publisher
- O'reilly Media Aug 2025
- Publication year
- 2025
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1098168038
- ISBN 13
- 9781098168032
- Item weight
- 750 grams
- Dimensions
- 232x174x23 mm
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.
"Synopsis" may belong to another edition of this title.
About the Author
Natasha Latysheva is a biologist and machine learning practitioner who is currently a Senior Research Engineer at Google DeepMind, specializing in deep learning for genomics. With a PhD in computational biology from the University of Cambridge and experience across several machine learning domains, her expertise is in bridging the gap between biology and machine learning. She is passionate about machine learning education and making complex technical topics accessible and exciting.
"About the title" may belong to another edition of this title.
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