Handwritten Digit Recognition Using by Kumar Akshi (7 results)

Paperback. Condition: Brand New. 80 pages. 8.66x5.91x0.19 inches. In Stock.

Taschenbuch. Condition: Neu. Neuware -The automated handwritten digit recognition task using supervised learning (classification) has many practical applications such as online handwriting recognition on electronic devices, recognizing postal mail codes for mail sorting, processing bank cheque amounts, and numeric entries in various forms filled manually and so on. Though the task is relatively a simple machine learning task, that is, the input consists of black and white pixels well separated from background which are categorized into output categories but has varied challenges associated.Deep learning can be applied to study multilevel representations of data before proceeding with classification. In the work presented in this book we compare various approaches and their variations to generate an optima set of features which can be used for the classification problem of handwritten digits.Books on Demand GmbH, Überseering 33, 22297 Hamburg 80 pp. Englisch.

Taschenbuch. Condition: Neu. Handwritten Digit Recognition Using Deep Learning | Akshi Kumar | Taschenbuch | 80 S. | Englisch | 2017 | LAP LAMBERT Academic Publishing | EAN 9786202024846 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

paperback. Condition: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The automated handwritten digit recognition task using supervised learning (classification) has many practical applications such as online handwriting recognition on electronic devices, recognizing postal mail codes for mail sorting, processing bank cheque amounts, and numeric entries in various forms filled manually and so on. Though the task is relatively a simple machine learning task, that is, the input consists of black and white pixels well separated from background which are categorized into output categories but has varied challenges associated.Deep learning can be applied to study multilevel representations of data before proceeding with classification. In the work presented in this book we compare various approaches and their variations to generate an optima set of features which can be used for the classification problem of handwritten digits. 80 pp. Englisch.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Kumar AkshiAkshi Kumar is a Ph.D in Computer Engineering from the University of Delhi, Delhi, India and currently working as an Assistant Professor in Department of Computer Science & Engineering at the Delhi Technological University.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The automated handwritten digit recognition task using supervised learning (classification) has many practical applications such as online handwriting recognition on electronic devices, recognizing postal mail codes for mail sorting, processing bank cheque amounts, and numeric entries in various forms filled manually and so on. Though the task is relatively a simple machine learning task, that is, the input consists of black and white pixels well separated from background which are categorized into output categories but has varied challenges associated.Deep learning can be applied to study multilevel representations of data before proceeding with classification. In the work presented in this book we compare various approaches and their variations to generate an optima set of features which can be used for the classification problem of handwritten digits.