AI Book Club: Deep Learning for Biology - November 2025 скачать в хорошем качестве

AI Book Club: Deep Learning for Biology - November 2025

Join events live: https://luma.com/ai-builders-and-lear... November's book is " Deep Learning for Biology"! This is a casual-style event. Not a structured presentation on topics. Sometimes, the discussion even drifts away from the chapters, but feel free to grab the mic to help steer it back. Feel free to join the discussion even if you have not read the book chapters! :) Want to discuss the contents during the reading week? Join the Slack Flyte MLOps Slack group and search for the "ai-reading-club" channel. https://slack.flyte.org/ ------------------------------------------------- About the book: Title: Deep Learning for Biology Authors: Charles Ravarani, Natasha Latysheva Published: July 2025 https://learning.oreilly.com/library/... Chapters: 1. Introduction 2. Learning the Language of Proteins 3. Learning the Logic of DNA 4. Understanding Drug–Drug Interactions Using Graphs 5. Detecting Skin Cancer in Medical Images 6. Learning Spatial Organization Patterns Within Cells 7. Tips and Tricks for Deep Learning in Biology Book 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 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. https://learning.oreilly.com/library/...