深度学习在医学图像分析中的应用，第2版

English | 2023 | ISBN: 032385124X | 544 pages | True PDF EPUB | 80.12 MB

This book is a detailed reference guide on deep learning and its applications. It aims to provide a basic understanding of deep learning and its different architectures that are applied to process images, speech, and natural language. It explains basic concepts and many modern use cases through fifteen chapters contributed by computer science academics and researchers. By the end of the book, the reader will become familiar with different deep learning approaches and models, and understand how to implement various deep learning algorithms using multiple frameworks and libraries. This book is divided into three parts. The first part explains the basic operating understanding, history, evolution, and challenges associated with deep learning. The basic concepts of mathematics and the hardware requirements for deep learning implementation, and some of its popular frameworks for medical applications are also covered. The second part is dedicated to sentiment analysis using deep learning and machine learning techniques. This book section covers the experimentation and application of deep learning techniques and architectures in real-world applications. It details the salient approaches, issues, and challenges in building ethically aligned machines. An approach inspired by traditional Eastern thought and wisdom is also presented. The final part covers artificial intelligence approaches used to explain the machine learning models that enhance transparency for the benefit of users. A review and detailed description of the use of knowledge graphs in generating explanations for black-box recommender systems and a review of ethical system design and a model for sustainable education is included in this section. An additional chapter demonstrates how a semi-supervised machine learning technique can be used for cryptocurrency portfolio management. The book is a timely reference for academicians, professionals, researchers and students at engineering and medical institutions working on artificial intelligence applications.

这本书是一部详细的深度学习及其应用指南。它的目标是为读者提供对深度学习基本概念和其应用于处理图像、语音和自然语言的不同架构的基本理解。通过十五章由计算机科学学者和研究人员贡献的内容，解释了基本概念以及许多现代应用场景。在本书结束时，读者将熟悉各种深度学习的方法和模型，并了解如何使用多种框架和库实现各种深度学习算法。这本书分为三部分。第一部分解释了与深度学习相关的基本操作理解、历史演变及其面临的挑战。还涵盖了数学基础以及用于医疗应用的深度学习实现硬件要求，一些流行的框架也进行了介绍。第二部分专注于利用深度学习和机器学习技术进行情感分析。这一部分内容涵盖了在真实世界应用程序中实验和运用深度学习技术和架构。它详细介绍了构建伦理对齐机器时的关键方法、问题和挑战。还提出了一种基于传统东方思想和智慧的方法。最后一部分讨论了用于增强透明度以造福用户的机器学习模型的AI方法。本节还包括了使用知识图谱生成黑盒推荐系统的解释的审查和详细的描述，以及关于道德系统设计的回顾及其可持续教育模型。这一章节额外的一章演示了半监督机器学习技术如何用于加密货币投资组合管理。这本书是工程和医学机构上的人工智能应用人员、学者、专业人员和学生的重要参考书籍。

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