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.