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- Author: Charles Ravarani
- Publisher: O'Reilly Media
- ISBN-10: 1098168038
- ISBN-13: 9781098168032
- Format: 17.8 x 23.3 x 2.3 cm, minkšti viršeliai
- Language: English
- SAVE -10% with code: EXTRA
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Description
Bridge the gap between biology and cutting-edge machine learning techniques with this essential guide. Train and interpret deep learning models and answer fundamental biological questions. Whether your background is in biology, machine learning, or software engineering, this book empowers you with the practical skills needed for your deep learning journey.
Through step-by-step recipes, authors Natasha Latysheva and Charles Ravarani teach you to train models for tasks like understanding gene regulatory logic, uncovering insights about protein families, and exploring subcellular localization. Each chapter provides self-contained mini-projects that serve as templates for approaching complex biological problems.
By the end of this book, you'll be able to:
- Approach biological problems with a deep learning perspective
- Train and interpret deep learning models to explore complex biological data
- Leverage Python code and iPython notebooks to extend your projects
- Transform your insights into actionable solutions in medicine, genomics, and biotechnology
EXTRA 10 % discount with code: EXTRA
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- Author: Charles Ravarani
- Publisher: O'Reilly Media
- ISBN-10: 1098168038
- ISBN-13: 9781098168032
- Format: 17.8 x 23.3 x 2.3 cm, minkšti viršeliai
- Language: English English
Bridge the gap between biology and cutting-edge machine learning techniques with this essential guide. Train and interpret deep learning models and answer fundamental biological questions. Whether your background is in biology, machine learning, or software engineering, this book empowers you with the practical skills needed for your deep learning journey.
Through step-by-step recipes, authors Natasha Latysheva and Charles Ravarani teach you to train models for tasks like understanding gene regulatory logic, uncovering insights about protein families, and exploring subcellular localization. Each chapter provides self-contained mini-projects that serve as templates for approaching complex biological problems.
By the end of this book, you'll be able to:
- Approach biological problems with a deep learning perspective
- Train and interpret deep learning models to explore complex biological data
- Leverage Python code and iPython notebooks to extend your projects
- Transform your insights into actionable solutions in medicine, genomics, and biotechnology
Reviews