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Deep Learning For The Life Sciences: Applying Deep Learning To Genomics, Microscopy, Drug Discovery, And More
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Deep Learning Has Already Achieved Remarkable Results In Many Fields. Now It'S Making Waves Throughout The Sciences Broadly And The Life Sciences In Particular. This Practical Book Teaches Developers And Scientists How To Use Deep Learning For Genomics, Chemistry, Biophysics, Microscopy, Medical Analysis, And Other Fields.Ideal For Practicing Developers And Scientists Ready To Apply Their Skills To Scientific Applications Such As Biology, Genetics, And Drug Discovery, This Book Introduces Several Deep Network Primitives. You'Ll Follow A Case Study On The Problem Of Designing New Therapeutics That Ties Together Physics, Chemistry, Biology, And Medicinean Example That Represents One Of Science'S Greatest Challenges. Learn The Basics Of Performing Machine Learning On Molecular Data Understand Why Deep Learning Is A Powerful Tool For Genetics And Genomics Apply Deep Learning To Understand Biophysical Systems Get A Brief Introduction To Machine Learning With Deepchem Use Deep Learning To Analyze Microscopic Images Analyze Medical Scans Using Deep Learning Techniques Learn About Variational Autoencoders And Generative Adversarial Networks Interpret What Your Model Is Doing And How It'S Working
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- Q: How many pages does the book have? A: This book contains two hundred thirty-three pages. It provides in-depth insights into applying deep learning in the life sciences.
- Q: What is the binding type of this book? A: The book is available in paperback binding. This format is ideal for ease of handling and reading.
- Q: What are the dimensions of the book? A: The book measures six point eight one inches in length, zero point six inches in width, and nine point zero two inches in height. These dimensions make it portable.
- Q: Who is the author of this book? A: The author of this book is Bharath Ramsundar. He is known for his work in deep learning applications in science.
- Q: What is the primary subject matter of the book? A: The book focuses on deep learning applications in the life sciences. It covers topics like genomics, microscopy, and drug discovery.
- Q: How can I apply the concepts in this book? A: Readers can apply deep learning concepts to fields such as biology, genetics, and drug discovery. The book includes case studies for practical understanding.
- Q: Is this book suitable for beginners? A: Yes, the book is suitable for both beginners and practicing developers. It introduces deep learning concepts in an accessible manner.
- Q: Can I use this book for medical analysis? A: Yes, the book covers deep learning techniques applicable to medical analysis. It includes insights into analyzing medical scans.
- Q: What kind of case studies does this book include? A: The book includes a case study on designing new therapeutics. This example integrates physics, chemistry, biology, and medicine.
- Q: Does the book discuss machine learning basics? A: Yes, it provides a brief introduction to machine learning concepts, particularly using DeepChem. This is beneficial for those new to the field.
- Q: What deep learning frameworks are mentioned in the book? A: The book discusses frameworks like variational autoencoders and generative adversarial networks. These are crucial for advanced deep learning applications.
- Q: How do I interpret the models discussed in the book? A: The book teaches readers how to interpret what their models are doing. Understanding model functionality is emphasized for practical application.
- Q: Is there a focus on analyzing microscopic images? A: Yes, the book covers using deep learning to analyze microscopic images. This is essential for researchers in the life sciences.
- Q: Are there practical examples for drug discovery? A: Yes, the book provides practical examples related to drug discovery. These examples help illustrate the application of deep learning in real-world scenarios.
- Q: What is the publisher of this book? A: The book is published by O'Reilly Media. They are well-known for their educational resources in technology and programming.