Deep Learning in Data Analytics: Recent Techniques, Practices and Applications: Studies in Big Data, cartea 91
Editat de Debi Prasanna Acharjya, Anirban Mitra, Noor Zamanen Limba Engleză Paperback – 13 aug 2022
Deep learning approaches prevent loss of information and hence enhance the performance of data analysis and learning techniques. It brings up many research issues in the industry and research community to capture and access data effectively. The book provides the conceptual basis of deep learning required to achieve in-depth knowledge in computer and data science. It has been done to make the book more flexible and to stimulate further interest in topics. All these help researchers motivate towards learning and implementing the concepts in real-life applications.
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Paperback (1) | 1157.43 lei 6-8 săpt. | |
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Springer International Publishing – 12 aug 2021 | 1163.72 lei 6-8 săpt. |
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Specificații
ISBN-13: 9783030758578
ISBN-10: 3030758575
Pagini: 266
Ilustrații: XX, 266 p. 114 illus., 92 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.41 kg
Ediția:1st ed. 2022
Editura: Springer International Publishing
Colecția Springer
Seria Studies in Big Data
Locul publicării:Cham, Switzerland
ISBN-10: 3030758575
Pagini: 266
Ilustrații: XX, 266 p. 114 illus., 92 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.41 kg
Ediția:1st ed. 2022
Editura: Springer International Publishing
Colecția Springer
Seria Studies in Big Data
Locul publicării:Cham, Switzerland
Cuprins
Study on Discrete Action Sequences using Deep Emotional Intelligence.- A Novel Noise Removal Technique Influenced by Deep Convolutional Autoencoders on Mammograms.- A High Security Framework through Human Brain using Algo Mixture Model Deep Learning Algorithm.- Knowledge Framework for Deep Learning: Congenital Heart Disease.- Computing System and Machine Learning.- Automatic Image Segmentation by Ranking based SVM in Convolutional Neural Network on Diabetic Fundus Image.
Notă biografică
Textul de pe ultima copertă
This book comprises theoretical foundations to deep learning, machine learning and computing system, deep learning algorithms, and various deep learning applications. The book discusses significant issues relating to deep learning in data analytics. Further in-depth reading can be done from the detailed bibliography presented at the end of each chapter. Besides, this book's material includes concepts, algorithms, figures, graphs, and tables in guiding researchers through deep learning in data science and its applications for society.
Deep learning approaches prevent loss of information and hence enhance the performance of data analysis and learning techniques. It brings up many research issues in the industry and research community to capture and access data effectively. The book provides the conceptual basis of deep learning required to achieve in-depth knowledge in computer and data science. It has been done to make the book more flexible and to stimulate further interest in topics. All these help researchers motivate towards learning and implementing the concepts in real-life applications.
Deep learning approaches prevent loss of information and hence enhance the performance of data analysis and learning techniques. It brings up many research issues in the industry and research community to capture and access data effectively. The book provides the conceptual basis of deep learning required to achieve in-depth knowledge in computer and data science. It has been done to make the book more flexible and to stimulate further interest in topics. All these help researchers motivate towards learning and implementing the concepts in real-life applications.
Caracteristici
Provides recent advances in the fields of Deep Learning Presents theoretical advances and its applications to real-life problems Offers concepts and techniques of deep learning in a precise and clear manner