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Practical AI for healthcare professionals :machine learning with Numpy, Scikit-learn, and TensorFlow /

紀錄類型:	書目-語言資料,印刷品 : 單行本
正題名/作者:	Practical AI for healthcare professionals :/ Abhinav Suri.
其他題名:	machine learning with Numpy, Scikit-learn, and TensorFlow /
作者:	Suri, Abhinav.
出版者:	[United States] :Apress,c2022.
面頁冊數:	xiv, 254 p. :ill. ;24 cm.
附註:	Includes index.
標題:	Artificial intelligence - Medical applications. -
ISBN:	9781484277799$q(pbk.) :
ISBN:	1484277791$q(pbk.)

Practical AI for healthcare professionals :machine learning with Numpy, Scikit-learn, and TensorFlow /
Suri, Abhinav.

Practical AI for healthcare professionals :machine learning with Numpy, Scikit-learn, and TensorFlow /Abhinav Suri. - 1st ed. - [United States] :Apress,c2022. - xiv, 254 p. :ill. ;24 cm.

Includes index.

Chapter 1: Introduction to AI and its Use Cases- Chapter 2: Computational Thinking -- Chapter 3: Overview of Programming -- Chapter 4: A Brief Tour of Machine Learning Algorithms. -Chapter 5: Project #1 Neural Networks & Heart Disease -- Chapter 6: Project #2 CNNs & Brain Tumor Detection -- Chapter 7: The Future of Healthcare and AI.

Use Artificial Intelligence (AI) to analyze and diagnose what previously could only be handled by trained medical professionals. This book gives an introduction to practical AI, focusing on real-life medical problems, how to solve them with actual code, and how to evaluate the efficacy of these solutions. You'll start by learning how to diagnose problems as ones that can and cannot be solved with AI or computer science algorithms. If you're not familiar with those algorithms, that's not a problem. You'll learn the basics of algorithms and neural networks and when each should be applied. Then you'll tackle the essential parts of basic Python programming relevant to data processing and making AI programs. The TensorFlow library alogn with Numpy and Scikit-Learn are covered, too. Once you've mastered those basic computer science concepts, you can dive into three projects with code, implementation details and explanation, and diagnostic utility analysis. These projects give you the change to explore using machine learning algorithms for diagnosing diabetes from patient data, using basic neural networks for heart disease prediction from cardiac data, and using convolutional networks for brain tumor segmentation from MRI scans The topics and projects covered not only encompass areas of the medical field where AI is already playing a major role but also are engineered to cover as much as possible of AI that is relevant to medical diagnostics. Along the way, readers can expect to learn data processing, how to conceptualize problems that can be solved by AI, and how to program solutions to problems using modern libraries, such as TensorFlow. Physicians and other healthcare professionals who can master these skills will be able to lead AI-based research and diagnostic tool development, ultimately benefiting countless patients. What You'll Learn Distinguish between problems that currently can and cannot be solved with AI Master programming concepts not familiar to physicians, such as libraries, coding, and creating and training ML models Perform dataset analysis with decision trees, SVMs, and neural networks. Who This Book Is For Physicians and other healthcare professionals curious about AI and interested in leading medical innovation initiatives. Additionally, software engineers working on healthcare related projects involving AI.

ISBN: 9781484277799$q(pbk.) :NT1340Subjects--Topical Terms:

165297
Artificial intelligence
--Medical applications.

LC Class. No.: R859.7.A78

Dewey Class. No.: 610.285/63

Practical AI for healthcare professionals :machine learning with Numpy, Scikit-learn, and TensorFlow /
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