March 16, 2026
Deep Learning for Biology Harness AI to Solve Real-World Biology Problems (TrueRetail EPUB)
Deep Learning for Biology Harness AI to Solve Real-World Biology Problems (TrueRetail EPUB) | 14.9 MB
Title: Deep Learning for Biology
Author: Charles Ravarani
Category: Nonfiction, Computers, Application Software, Business Software, Advanced Computing, Artificial Intelligence, General Computing
Language: English | ISBN: 9781098167998
Description:
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…
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.
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