Deep Learning in Visual Computing and Signal Processing
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Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
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Product details:
- Edition number 1
- Publisher Apple Academic Press
- Date of Publication 20 October 2022
- ISBN 9781774638705
- Binding Hardback
- No. of pages288 pages
- Size 229x152 mm
- Weight 530 g
- Language English
- Illustrations 78 Illustrations, black & white; 2 Halftones, black & white; 76 Line drawings, black & white; 19 Tables, black & white 405
Categories
Short description:
Covers the fundamentals and advanced topics in designing and deploying techniques using deep architectures and their application in visual computing and signal processing. It discusses deep learning in neural networks and for object recognition and detection models and the specific applications in visual and signal processing.
MoreLong description:
An enlightening amalgamation of deep learning concepts with visual computing and signal processing applications, this new volume covers the fundamentals and advanced topics in designing and deploying techniques using deep architectures and their application in visual computing and signal processing.
The volume first lays out the fundamentals of deep learning as well as deep learning architectures and frameworks. It goes on to discuss deep learning in neural networks and deep learning for object recognition and detection models. It looks at the various specific applications of deep learning in visual and signal processing, such as in biorobotics, for automated brain tumor segmentation in MRI images, in neural networks for use in seizure classification, for digital forensic investigation based on deep learning, and more.
MoreTable of Contents:
1. Deep Learning Architecture and Framework 2. Deep Learning in Neural Networks: An Overview 3. Deep Learning: Current Trends and Techniques 4. TensorFlow: Machine Learning Using Heterogeneous Edge on Distributed Systems 5. Introduction to Biorobotics: Part of Biomedical Signal Processing 6. Deep Learning-Based Object Recognition and Detection Model 7. Deep Learning: A Pathway for Automated Brain Tumor Segmentation in MRI Images 8. Recurrent Neural Networks and Their Application in Seizure Classification 9. Brain Tumor Classification Using Convolutional Neural Network 10. A Proactive Improvement Toward Digital Forensic Investigation Based on Deep Learning
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