AI in the Lab: A Deep Learning Researcher's Starter Pack

Nabeeha Abid

ISBN 10: 6205640775 / ISBN 13: 9786205640777

Published by LAP LAMBERT Academic Publishing Apr 2025, 2025

About this Item

Neuware -Artificial Intelligence in the Lab: A Deep Learning Researcher's Starter Pack' is a practical and beginner-friendly guide that bridges deep learning with molecular biology research. It is designed for biology researchers, students, and early-stage AI enthusiasts who want to apply deep learning to biological sequences, structure prediction, and functional genomics. The book covers essential foundations of deep learning, experimental design, model building, evaluation, and real-world biological applications, including sequence analysis, protein structure prediction, and generative modeling. With clear explanations, hands-on code examples, and valuable research advice, this book empowers researchers to confidently integrate AI into their molecular biology projects.Books on Demand GmbH, Überseering 33, 22297 Hamburg 100 pp. Englisch.

Seller Inventory # 9786205640777

Bibliographic Details

• Title: AI in the Lab: A Deep Learning Researcher's Starter Pack
• Publisher: LAP LAMBERT Academic Publishing Apr 2025
• Publication Date: 2025
• Language: English
• ISBN 10: 6205640775
• ISBN 13: 9786205640777
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 6D millimeters 220 millimeters width by 150 millimeters height by 6 millimeters depth
• Weight: 167 grams

About this title

Synopsis

Artificial Intelligence in the Lab: A Deep Learning Researcher's Starter Pack" is a practical and beginner-friendly guide that bridges deep learning with molecular biology research. It is designed for biology researchers, students, and early-stage AI enthusiasts who want to apply deep learning to biological sequences, structure prediction, and functional genomics. The book covers essential foundations of deep learning, experimental design, model building, evaluation, and real-world biological applications, including sequence analysis, protein structure prediction, and generative modeling. With clear explanations, hands-on code examples, and valuable research advice, this book empowers researchers to confidently integrate AI into their molecular biology projects.