artificial learning based convolution von bukasa muepu (13 Ergebnisse)

Paperback. Zustand: new. Paperback. Numerical recognition of bank cheques presents a major challenge and plays an important role in today's world, as machines must be able to learn like humans and solve complex problems such as recognizing the digits of bank cheques. Despite attempts to make machines learn like humans, no machine has yet been able to recognize 100% of handwritten digits. Despite attempts to make machines capable of learning like humans, no machine is yet 100% capable of recognizing handwritten digits. It aims to build a prediction model called a classifier that will facilitate this recognition from data in the MNIST database, with a view to possibly helping banks to speed up the processing of banking transactions by cheque.The approach proposed here essentially consists of two steps: feature extraction and classification of image pixels using a convolutional neural network, one of the deep learning algorithms with a proven track record in image processing. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Zustand: New.

Zustand: New. In.

Zustand: New.

Paperback. Zustand: new. Paperback. Numerical recognition of bank cheques presents a major challenge and plays an important role in today's world, as machines must be able to learn like humans and solve complex problems such as recognizing the digits of bank cheques. Despite attempts to make machines learn like humans, no machine has yet been able to recognize 100% of handwritten digits. Despite attempts to make machines capable of learning like humans, no machine is yet 100% capable of recognizing handwritten digits. It aims to build a prediction model called a classifier that will facilitate this recognition from data in the MNIST database, with a view to possibly helping banks to speed up the processing of banking transactions by cheque.The approach proposed here essentially consists of two steps: feature extraction and classification of image pixels using a convolutional neural network, one of the deep learning algorithms with a proven track record in image processing. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Paperback. Zustand: new. Paperback. Numerical recognition of bank cheques presents a major challenge and plays an important role in today's world, as machines must be able to learn like humans and solve complex problems such as recognizing the digits of bank cheques. Despite attempts to make machines learn like humans, no machine has yet been able to recognize 100% of handwritten digits. Despite attempts to make machines capable of learning like humans, no machine is yet 100% capable of recognizing handwritten digits. It aims to build a prediction model called a classifier that will facilitate this recognition from data in the MNIST database, with a view to possibly helping banks to speed up the processing of banking transactions by cheque.The approach proposed here essentially consists of two steps: feature extraction and classification of image pixels using a convolutional neural network, one of the deep learning algorithms with a proven track record in image processing. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

PAP. Zustand: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Zustand: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 64 pp. Englisch.

Zustand: New. Print on Demand.

Zustand: New. PRINT ON DEMAND.

Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Numerical recognition of bank cheques presents a major challenge and plays an important role in today's world, as machines must be able to learn like humans and solve complex problems such as recognizing the digits of bank cheques. Despite attempts to make machines learn like humans, no machine has yet been able to recognize 100% of handwritten digits. Despite attempts to make machines capable of learning like humans, no machine is yet 100% capable of recognizing handwritten digits. It aims to build a prediction model called a classifier that will facilitate this recognition from data in the MNIST database, with a view to possibly helping banks to speed up the processing of banking transactions by cheque.The approach proposed here essentially consists of two steps: feature extraction and classification of image pixels using a convolutional neural network, one of the deep learning algorithms with a proven track record in image processing.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 64 pp. Englisch.

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Numerical recognition of bank cheques presents a major challenge and plays an important role in today's world, as machines must be able to learn like humans and solve complex problems such as recognizing the digits of bank cheques. Despite attempts to make machines learn like humans, no machine has yet been able to recognize 100% of handwritten digits. Despite attempts to make machines capable of learning like humans, no machine is yet 100% capable of recognizing handwritten digits. It aims to build a prediction model called a classifier that will facilitate this recognition from data in the MNIST database, with a view to possibly helping banks to speed up the processing of banking transactions by cheque.The approach proposed here essentially consists of two steps: feature extraction and classification of image pixels using a convolutional neural network, one of the deep learning algorithms with a proven track record in image processing.