Challenges and Opportunities for Deep Learning Applications in Industry 4.0

Book Description

The competence of deep le宇联arning for the automation and manufacturing sector has received astonishing attention in re湾流cent times. The manufacturing industry has recently experienced a revolutionary advancement despite several issues. One of the limitations for technical progress is the bottleneck encountered due to the enormous increase in data volume for processing, comprising various formats, semantics, qualities and features. Deep learning enables detection of meaningful features that are difficult to perform using traditional methods.

The book takes the reader on a technological voyage of the industry 4.0 space. Chapters宝珀 highlight recent applications of deep learning and the associated challenges and opportunities it presents for automating industrial processes and smart applications.

Chapters introduce the re宇路表ader to a broad range of topics in deep learning and machine learning. Several deep learning techniques used by industrial professionals are covered, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence豪度 modeling, and practical project methodology. Readers will find information on the value of deep learning in applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.

The book also discusses prospective research directions that focus on the theory and practical applications of deep learning in industrial automation. Therefore, the book aims to serve as a comprehensive reference guide for industrial consultants interested in industry 4.0, and as a handbook for beginners in data science and advanced computer science courses.

中文：

书名：行业4.0中深度学习应用的挑战和机遇

近年来，自动化和制造业的深度学习能力受到了惊人的关注。尽管有几个问题，制造业最近经历了革命性的进步。技术进步的局限性之一是由于处理数据量的巨大增加而遇到的瓶颈，包括各种格式，语义，质量和特征。深度学习可以检测到使用传统方法难以执行的有意义的功能。

本书将读者带入行业4.0空间的技术航行。各章重点介绍了深度学习的最新应用以及它为自动化工业流程和智能应用程序带来的相关挑战和机遇。

章节向读者介绍了深度学习和机器学习的广泛主题。涵盖了工业专业人员使用的几种深度学习技术，包括深度前馈网络，正则化，优化算法，卷积网络，序列建模和实际项目方法。读者将在自然语言处理，语音识别，计算机视觉，在线推荐系统，生物信息学和视频游戏等应用中找到有关深度学习价值的信息。

本书还讨论了前瞻性研究方向，重点是深度学习在工业自动化中的理论和实际应用。因此，本书旨在为对行业4.0感兴趣的工业顾问提供全面的参考指南，并为数据科学和高级计算机科学课程的初学者提供手册。