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18.0 MB
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9F9E463435639023CA13F8DC7BCD5C16ADE2B599
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July 20, 2025, 10:31 a.m.
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(Last updated: July 20, 2025, 10:32 a.m.)
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| Ravarani C. Deep Learning for Biology. Harness AI...Biology Problems 2025.pdf | 18.0 MB |
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SOURCE: Ravarani C. Deep Learning for Biology. Harness AI...Biology Problems 2025
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COVER
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MEDIAINFO
Textbook in PDF format 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 Prerequisites: We assume a basic familiarity with Python and the core scientific Python libraries—namely, NumPy, pandas, and Matplotlib. You don’t need to be an expert programmer, but you should feel comfortable reading and writing basic Python code. Some background in Machine Learning is also helpful. Concepts like optimization, loss functions, training steps, overfitting, and feedforward neural networks will come up frequently. If these are unfamiliar, don’t worry—brief explanations are included throughout the book, and you can always pause to brush up using external resources as needed. Who This Book Is For: This book is for anyone interested in using Deep Learning to solve biological problems—whether you’re a biologist, a data scientist, or a software engineer. We have aimed to provide an accessible bridge between disciplines and a practical foundation for working in this emerging field. We’ve designed the book to be user-friendly and interdisciplinary, with the goal of supporting readers from a wide variety of backgrounds
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