Convolutional Neural Networks in Visual Computing A Concise Guide 1st Edition by Ragav Venkatesan, Baoxin Li 1138747955 9781138747951

Convolutional Neural Networks in Visual Computing: A Concise Guide 1st Edition by Ragav Venkatesan, Baoxin Li  – Ebook PDF Instant Download/DeliveryISBN: 1138747955, 9781138747951

Full download Convolutional Neural Networks in Visual Computing: A Concise Guide 1st Edition after payment.

Product details:

ISBN-10 :  1138747955 

ISBN-13 :  9781138747951

Author:   Ragav Venkatesan, Baoxin Li 

This book covers the fundamentals in designing and deploying techniques using deep architectures. It is intended to serve as a beginner’s guide to engineers or students who want to have a quick start on learning and/or building deep learning systems. This book provides a good theoretical and practical understanding and a complete toolkit of basic information and knowledge required to understand and build convolutional neural networks (CNN) from scratch. The book focuses explicitly on convolutional neural networks, filtering out other material that co-occur in many deep learning books on CNN topics.

Convolutional Neural Networks in Visual Computing: A Concise Guide 1st Table of contents:

Chapter 1 Introduction to Visual Computing
Image Representation Basics
Transform-Domain Representations
Image Histograms
Image Gradients and Edges
Going beyond Image Gradients
Line Detection Using the Hough Transform
Harris Corners
Scale-Invariant Feature Transform
Histogram of Oriented Gradients
Decision-Making in a Hand-Crafted Feature Space
Bayesian Decision-Making
Decision-Making with Linear Decision Boundaries
A Case Study with Deformable Part Models
Migration toward Neural Computer Vision
Summary
References
Chapter 2 Learning as a Regression Problem
Supervised Learning
Linear Models
Least Squares
Maximum-Likelihood Interpretation
Extension to Nonlinear Models
Regularization
Cross-Validation
Gradient Descent
Geometry of Regularization
Nonconvex Error Surfaces
Stochastic, Batch, and Online Gradient Descent
Alternative Update Rules Using Adaptive Learning Rates
Momentum
Summary
References
Chapter 3 Artificial Neural Networks
The Perceptron
Multilayer Neural Networks
The Back-Propagation Algorithm
Improving BP-Based Learning
Activation Functions
Weight Pruning
Batch Normalization
Summary
References
Chapter 4 Convolutional Neural Networks
Convolution and Pooling Layer
Convolutional Neural Networks
Summary
References
Chapter 5 Modern and Novel Usages of CNNs
Pretrained Networks
Generality and Transferability
Using Pretrained Networks for Model Compression
Mentee Networks and FitNets
Application Using Pretrained Networks: Image Aesthetics Using CNNs
Generative Networks
Autoencoders
Generative Adversarial Networks
Summary
References
Appendix A Yaan
Structure of Yann
Quick Start with Yann: Logistic Regression
Multilayer Neural Networks
Convolutional Neural Network
Autoencoder
Summary
References
Postscript
References

People also search for Convolutional Neural Networks in Visual Computing: A Concise Guide 1st:

convolutional neural networks for visual recognition
    
convolutional neural network computer vision
    
convolutional neural network visual
    
convolutional neural networks are used in applications such as
    
convolutional neural networks are used in what sorts of applications

Tags: Neural Networks, Visual Computing, Concise Guide, Ragav Venkatesan, Baoxin Li