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Practical Computer Vision Applications Using Deep Learning with CNNs
Af: Ahmed Fawzy Gad Engelsk Paperback
SPAR
kr 168
Practical Computer Vision Applications Using Deep Learning with CNNs
Af: Ahmed Fawzy Gad Engelsk Paperback
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 For
Data scientists, machine learning and deep learning engineers, software developers.

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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 For
Data scientists, machine learning and deep learning engineers, software developers.

Produktdetaljer
Sprog: Engelsk
Sider: 405
ISBN-13: 9781484241660
Indbinding: Paperback
Udgave:
ISBN-10: 1484241665
Kategori: Computer vision
Udg. Dato: 6 dec 2018
Længde: 25mm
Bredde: 181mm
Højde: 254mm
Oplagsdato: 6 dec 2018
Forfatter(e): Ahmed Fawzy Gad
Forfatter(e) Ahmed Fawzy Gad


Kategori Computer vision


Sprog Engelsk


Indbinding Paperback


Sider 405


Udgave


Længde 25mm


Bredde 181mm


Højde 254mm

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