AI for Healthcare with Keras and Tensorflow 2.0: Design, Develop, and Deploy Machine Learning Models Using Healthcare Data - Brossura

Anshik

Brossura

ISBN 10: 1484270851 ISBN 13: 9781484270851

Casa editrice: Apress, 2021

Riassunto

Learn how AI impacts the healthcare ecosystem through real-life case studies with TensorFlow 2.0 and other machine learning (ML) libraries.

This book begins explaining the dynamics of the healthcare market, including the role of stakeholders such as healthcare professionals, patients, and related terminologies in simple terms. You then will move on to ML applications in healthcare using case studies with coding. This is followed by looking at datasets in healthcare—EHR data with multi-task deep learning (DL). Next, you will go over a neurolinguistic programming (NLP) case study covering transfer learning in NLP. You also will be exposed to the challenges of applying modern ML techniques to highly sensitive data in healthcare using federated learning. There is a chapter on medical imaging analysis showing you how to deal with 2D and 3D images. The concluding section shows you how to build a Q&A system using a pre-trained transformer model. It also discusses Bio-Bert architecture to train your own Q&A system. And, lastly, you go through an ML application made live with Docker using Django.

By the end of this book, you will have a clear understanding of how the healthcare system works and how to apply ML and DL  tools and techniques to the healthcare industry.

What You Will Learn

• Understand the healthcare industry
• Design, develop, train, validate, and deploy machine learning models on healthcare data
• Be familiar with best practices for debugging and validating machine learning models
• Know how transfer learning and federated learning differ

Who This Book Is For

Data scientists and software developers interested in machine learning and its application in the healthcare industry

Su questo libro

Informazioni sull?autore

Anshik Bansal has a deep passion for building and shipping data science solutions that create great business value. He is currently working as a senior data scientist at ZS Associates and is a key member on the team developing core unstructured data science capabilities and products. He has worked across industries such as pharma, finance, and retail, with a focus on advanced analytics. Besides his day-to-day activities, which involve researching and developing AI solutions for client impact, he works with startups as a data science strategy consultant. Anshik holds a bachelor’s degree from Birla Institute of Technology & Science, Pilani. He is a regular speaker at AI and machine learning conferences. He enjoys trekking and cycling.

This book begins explaining the dynamics of the healthcare market, including the role of stakeholders such as healthcare professionals, patients, and related terminologies in simple terms. You then will move on to ML applications in healthcare using case studies with coding. This is followed by looking at datasets in healthcare EHR data with multi-task deep learning (DL). Next, you will go over a neurolinguistic programming (NLP) case study covering transfer learning in NLP. You also will be exposed to the challenges of applying modern ML techniques to highly sensitive data in healthcare using federated learning. There is a chapter on medical imaging analysis showing you how to deal with 2D and 3D images. The concluding section shows you how to build a Q&A system using a pre-trained transformer model. It also discusses Bio-Bert architecture to train your own Q&A system. And, lastly, you go through an ML application made live with Docker using Django.

By the end of this book, you will have a clear understanding of how the healthcare system works and how to apply ML and DL  tools and techniques to the healthcare industry.

You will:

• Understand the healthcare industry
• Design, develop, train, validate, and deploy machine learning models on healthcare data
• Be familiar with best practices for debugging and validating machine learning models
• Know how transfer learning and federated learning differ

Dettagli dell'edizione

• Editore: Apress
• Data di pubblicazione: 2021
• Lingua: Inglese
• ISBN 10: 1484270851
• ISBN 13: 9781484270851
• Rilegatura: Copertina flessibile
• Numero edizione: 1
• Numero di pagine: 400
• Contatto del produttore: non disponibile
• Persona responsabile: non disponibile