Trends In Deep Learning Methodologies
Download Trends In Deep Learning Methodologies PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Trends In Deep Learning Methodologies book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Trends in Deep Learning Methodologies
Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems covers deep learning approaches such as neural networks, deep belief networks, recurrent neural networks, convolutional neural networks, deep auto-encoder, and deep generative networks, which have emerged as powerful computational models. Chapters elaborate on these models which have shown significant success in dealing with massive data for a large number of applications, given their capacity to extract complex hidden features and learn efficient representation in unsupervised settings. Chapters investigate deep learning-based algorithms in a variety of application, including biomedical and health informatics, computer vision, image processing, and more. In recent years, many powerful algorithms have been developed for matching patterns in data and making predictions about future events. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. Deep learning methods can deal with multiple levels of representation in which the system learns to abstract higher level representations of raw data. Earlier, it was a common requirement to have a domain expert to develop a specific model for each specific application, however, recent advancements in representation learning algorithms allow researchers across various subject domains to automatically learn the patterns and representation of the given data for the development of specific models. - Provides insights into the theory, algorithms, implementation and the application of deep learning techniques - Covers a wide range of applications of deep learning across smart healthcare and smart engineering - Investigates the development of new models and how they can be exploited to find appropriate solutions
Demystifying Emerging Trends in Machine Learning
Author: Pankaj Kumar Mishra
language: en
Publisher: Bentham Science Publishers
Release Date: 2025-02-17
Demystifying Emerging Trends in Machine Learning (Volume 2) offers a deep dive into emerging and trending topics in the field of machine learning (ML). This edited volume showcases several machine learning methods for a variety of tasks. A key focus of this volume is the application of text classification for cybersecurity, E-commerce, sentiment analysis, public health and web content analysis. The 49 chapters highlight a wide variety of machine learning methods including SVNs, K-Means Clustering, CNNs, DCNNs, among others. Each chapter includes accessible information through summaries, discussions and reference lists. This comprehensive volume is essential for students, researchers, and professionals eager to understand the emerging trends reshaping machine learning today.
Proceedings of 4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications
Author: Vinit Kumar Gunjan
language: en
Publisher: Springer Nature
Release Date: 2024-05-22
The book is a collection of the best-selected research papers presented at the International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications (ICMISC 2023) held in September 2023 at the CMR Institute of Technology, Hyderabad, Telangana, India. This book will contain articles on current trends in machine learning, the internet of things, and smart city applications, emphasizing multi-disciplinary research in the area of artificial intelligence and cyberphysical systems. The book is a great resource for scientists, research scholars, and PG students to formulate their research ideas and find future directions in these areas. Further, this book serves as a reference work to understand the latest technologies used by practice engineers across the globe.