An Introduction to Image Classification: From Designed Models to End-to-End Learning - Image 1

£39.90

Springer An Introduction to Image Classification: From Designed Models to End-to-End Learning

Name: An Introduction to Image Classification: From Designed Models to End-to-End Learning
Brand: Springer
Price: 39.9 GBP
Availability: InStock

AI & Machine Learning

It has never been this cheap. We have no record of a lower price.

£40 today · cheaper than every other day in the last 3 months

NEW HERE?

Amazon shows you one price. We show you all of them.

Tosheroon watches Amazon prices so you don't have to. Every product on Amazon has a price history — we make it visible. Set the price you'd actually pay, and we'll email you the second it gets there. No app, no account, one email.

WHAT'S ON THIS PAGE

↓ Price chart
when this has been cheap or pricey

↓ Forecast
where the price is heading next

↓ Statistics
all-time high & low, recent range

↑ Price alert
name your number, we'll email you

Price History & Forecast

Grey patches = out of stock. Cheaper = lower on the chart. Hover for exact prices.

Last 7 days • 7 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 7 days • 1 price levels

Days at Price

Price Analysis

Most common price: £40 (7 days, 100.0%)

Price range: £40 - £40

Price levels: 1 different prices over 7 days

Description

Image classification is a critical component in computer vision tasks and has numerous applications. Traditional methods for image classification involve feature extraction and classification in feature space. Current state-of-the-art methods utilize end-to-end learning with deep neural networks, where feature extraction and classification are integrated into the model. Understanding traditional image classification is important because many of its design concepts directly correspond to components of a neural network. This knowledge can help demystify the behavior of these networks, which may seem opaque at first sight. The book starts from introducing methods for model-driven feature extraction and classification, including basic computer vision techniques for extracting high-level semantics from images. A brief overview of probabilistic classification with generative and discriminative classifiers is then provided. Next, neural networks are presented as a means to learn a classification model directly from labeled sample images, with individual components of the network discussed. The relationships between network components and those of a traditional designed model are explored, and different concepts for regularizing model training are explained. Finally, various methods for analyzing what a network has learned are covered in the closing section of the book. The topic of image classification is presented as a thoroughly curated sequence of steps that gradually increase understanding of the working of a fully trainable classifier. Practical exercises in Python/Keras/Tensorflow have been designed to allow for experimental exploration of these concepts. In each chapter, suitable functions from Python modules are briefly introduced to provide students with the necessary tools to conduct these experiments.

Product Specifications

Brand: Springer
Format: paperback
ASIN: 981997884X
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 26 January 2025
Listed Since: 26 January 2025

Barcode

No barcode data available

Springer An Introduction to Image Classification: From Designed Models to End-to-End Learning

It has never been this cheap. We have no record of a lower price.

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Modern Computer Vision with PyTorch: A practical roadmap from deep learning fundamentals to advanced applications and Generative AI

Practical Computer Vision Applications Using Deep Learning with CNNs: With Detailed Examples in Python Using TensorFlow and Kivy

Practical Machine Learning for Computer Vision: End-to-End Machine Learning for Images

Deep Learning Models: A Practical Approach for Hands-On Professionals (Transactions on Computer Systems and Networks)

Computer Vision Methods for Fast Image Classiﬁcation and Retrieval: 821 (Studies in Computational Intelligence, 821)

Practical Deep Learning for Computer Vision with Python

Foundations of Computer Vision: Computational Geometry, Visual Image Structures and Object Shape Detection: 124 (Intelligent Systems Reference Library, 124)

Foundations of Computer Vision: Computational Geometry, Visual Image Structures and Object Shape Detection: 124 (Intelligent Systems Reference Library, 124)

Computer Vision Projects with PyTorch: Design and Develop Production-Grade Models

Modern Computer Vision with PyTorch: Explore deep learning concepts and implement over 50 real-world image applications

A Guide to Convolutional Neural Networks for Computer Vision (Synthesis Lectures on Computer Vision)

Advances in Computer Vision: Proceedings of the 2019 Computer Vision Conference (CVC), Volume 1: 943 (Advances in Intelligent Systems and Computing, 943)

Principles and Labs for Deep Learning

Practical Deep Learning, 2nd Edition: A Python-Based Introduction

Digital Image Processing and Analysis: Computer Vision and Image Analysis

Computer Vision -- ACCV 2009: 9th Asian Conference on Computer Vision, Xi'an, China, September 23-27, 2009, Revised Selected Papers, Part III: 5996 (Lecture Notes in Computer Science, 5996)

Computer Vision - ACCV 2006: 7th Asian Conference on Computer Vision, Hyderabad, India, January 13-16, 2006, Proceedings, Part I: 3851 (Lecture Notes in Computer Science, 3851)

Computer Vision: Algorithms and Applications (Texts in Computer Science)

Computer Vision: Algorithms and Applications (Texts in Computer Science)

Computer Vision – ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012. Proceedings, Part IV: 7575 (Lecture Notes in Computer Science, 7575)

Computer Vision – ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012. Proceedings, Part V: 7576 (Lecture Notes in Computer Science, 7576)

Infrared Small Target Detection: Theory, Methods, and Algorithms.

Computer Vision – ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part II: 7573 (Lecture Notes in Computer Science, 7573)

Computer Vision: Models, Learning, and Inference