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Practical Computer Vision Applications Using Deep Learning with CNNs: With Detailed Examples in Python Using TensorFlow and Kivy - Couverture souple
Synopsis
Deploy deep learning applications into production across multiple platforms. You will work on computer vision applications that use the convolutional neural network (CNN) deep learning model and Python. This book starts by explaining the traditional machine-learning pipeline, where you will analyze an image dataset. Along the way you will cover artificial neural networks (ANNs), building one from scratch in Python, before optimizing it using genetic algorithms.
For automating the process, the book highlights the limitations of traditional hand-crafted features for computer vision and why the CNN deep-learning model is the state-of-art solution. CNNs are discussed from scratch to demonstrate how they are different and more efficient than the fully connected ANN (FCNN). You will implement a CNN in Python to give you a full understanding of the model.After consolidating the basics, you will use TensorFlow to build a practical image-recognition model that you will deploy to a web server using Flask, making it accessible over the Internet. Using Kivy and NumPy, you will create cross-platform data science applications with low overheads.
This book will help you apply deep learning and computer vision concepts from scratch, step-by-step from conception to production. What You Will Learn
- Understand how ANNs and CNNs work
- Create computer vision applications and CNNs from scratch using Python
- Follow a deep learning project from conception to production using TensorFlow
- Use NumPy with Kivy to build cross-platform data science applications
Who This Book Is ForData scientists, machine learning and deep learning engineers, software developers.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
Ahmed Fawzy Gad is a teaching assistant at the Faculty of Computers and Information (FCI), Menoufia University, Egypt. He has done his MSc in Computer Science. Ahmed is interested in deep learning, machine learning, computer vision, and Python. He aims to add value to the data science community by sharing his writings and tutorials. He is the author of the book "Practical Computer Vision Applications Using Deep Learning with CNN's" published by Apress.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
- ÉditeurApress
- Date d'édition2018
- ISBN 10 1484241665
- ISBN 13 9781484241660
- ReliureBroché
- Langueanglais
- Numéro d'édition1
- Nombre de pages428
- Coordonnées du fabricantnon disponible
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Practical Computer Vision Applications Using Deep Learning with CNNs: With Detailed Examples in Python Using TensorFlow and Kivy
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Explains the basic concepts of deep learning using numerical examplesDiscusses the practical use of deep convolutional neural networks in computer vision with PythonCovers deploying trained models. N° de réf. du vendeur 252635095
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Deploy deep learning applications into production across multiple platforms. You will work on computer vision applications that use the convolutional neural network (CNN) deep learning model and Python.This bookstarts by explaining the traditional machine-learning pipeline, where you will analyze an image dataset. Along the way you will cover artificial neural networks (ANNs), building one from scratch in Python, before optimizing it using genetic algorithms.For automating the process, the book highlights the limitations of traditional hand-crafted features for computer vision and why the CNN deep-learning model is the state-of-art solution. CNNs are discussed from scratch to demonstrate how they are different and more efficient than the fully connected ANN (FCNN). You will implement a CNN in Python to give you a full understanding of the model.After consolidating the basics, you will use TensorFlow to build a practical image-recognition model that you will deploy to a web server using Flask, making it accessible over the Internet. Using Kivy and NumPy, you will create cross-platform data science applications with low overheads.This book will help you apply deep learning and computer vision concepts from scratch, step-by-step from conception to production.What You Will LearnUnderstand how ANNs and CNNs workCreate computer vision applications and CNNs from scratch using PythonFollow a deep learning project from conception to production using TensorFlowUse NumPy with Kivy to build cross-platform data science applicationsWho This Book Is ForData scientists, machine learning and deep learning engineers, software developers. 428 pp. Englisch. N° de réf. du vendeur 9781484241660
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Practical Computer Vision Applications Using Deep Learning with CNNs
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Apress, Apress Dez 2018, 2018
ISBN 10 : 1484241665
ISBN 13 : 9781484241660
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Deploy deep learning applications into production across multiple platforms. You will work on computer vision applications that use the convolutional neural network (CNN) deep learning model and Python. This book starts by explaining the traditional machine-learning pipeline, where you will analyze an image dataset. Along the way you will cover artificial neural networks (ANNs), building one from scratch in Python, before optimizing it using genetic algorithms.APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin 428 pp. Englisch. N° de réf. du vendeur 9781484241660
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Deploy deep learning applications into production across multiple platforms. You will work on computer vision applications that use the convolutional neural network (CNN) deep learning model and Python.This bookstarts by explaining the traditional machine-learning pipeline, where you will analyze an image dataset. Along the way you will cover artificial neural networks (ANNs), building one from scratch in Python, before optimizing it using genetic algorithms.For automating the process, the book highlights the limitations of traditional hand-crafted features for computer vision and why the CNN deep-learning model is the state-of-art solution. CNNs are discussed from scratch to demonstrate how they are different and more efficient than the fully connected ANN (FCNN). You will implement a CNN in Python to give you a full understanding of the model.After consolidating the basics, you will use TensorFlow to build a practical image-recognition model that you will deploy to a web server using Flask, making it accessible over the Internet. Using Kivy and NumPy, you will create cross-platform data science applications with low overheads.This book will help you apply deep learning and computer vision concepts from scratch, step-by-step from conception to production.What You Will LearnUnderstand how ANNs and CNNs workCreate computer vision applications and CNNs from scratch using PythonFollow a deep learning project from conception to production using TensorFlowUse NumPy with Kivy to build cross-platform data science applicationsWho This Book Is ForData scientists, machine learning and deep learning engineers, software developers. N° de réf. du vendeur 9781484241660
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