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Englisch
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Beschreibung
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
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.
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
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.
Über den Autor
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.
Zusammenfassung
Explains the basic concepts of deep learning using numerical examples
Discusses the practical use of deep convolutional neural networks in computer vision with Python
Covers deploying trained models
Inhaltsverzeichnis
1. Recognition in Computer Vision.- 2. Artificial Neural Network.- 3. Classification using ANN with Engineered Features.- 4. ANN Parameters Optimization.- 5. Convolutional Neural Networks.- 6. TensorFlow Recognition Application.- 7. Deploying Pre-Trained Models.- 8. Cross-Platform Data Science Applications.Appendix: Uploading Projects to PyPI.
Details
| Erscheinungsjahr: | 2018 |
|---|---|
| Genre: | Importe, Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: |
xxii
405 S. 200 s/w Illustr. 405 p. 200 illus. |
| ISBN-13: | 9781484241660 |
| ISBN-10: | 1484241665 |
| Sprache: | Englisch |
| Herstellernummer: | 978-1-4842-4166-0 |
| Ausstattung / Beilage: | Paperback |
| Einband: | Kartoniert / Broschiert |
| Autor: | Gad, Ahmed Fawzy |
| Auflage: | 1st ed. |
| Hersteller: |
Apress
Apress L.P. |
| Verantwortliche Person für die EU: | Books on Demand GmbH, In de Tarpen 42, D-22848 Norderstedt, info@bod.de |
| Maße: | 254 x 178 x 24 mm |
| Von/Mit: | Ahmed Fawzy Gad |
| Erscheinungsdatum: | 06.12.2018 |
| Gewicht: | 0,8 kg |
Über den Autor
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.
Zusammenfassung
Explains the basic concepts of deep learning using numerical examples
Discusses the practical use of deep convolutional neural networks in computer vision with Python
Covers deploying trained models
Inhaltsverzeichnis
1. Recognition in Computer Vision.- 2. Artificial Neural Network.- 3. Classification using ANN with Engineered Features.- 4. ANN Parameters Optimization.- 5. Convolutional Neural Networks.- 6. TensorFlow Recognition Application.- 7. Deploying Pre-Trained Models.- 8. Cross-Platform Data Science Applications.Appendix: Uploading Projects to PyPI.
Details
| Erscheinungsjahr: | 2018 |
|---|---|
| Genre: | Importe, Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: |
xxii
405 S. 200 s/w Illustr. 405 p. 200 illus. |
| ISBN-13: | 9781484241660 |
| ISBN-10: | 1484241665 |
| Sprache: | Englisch |
| Herstellernummer: | 978-1-4842-4166-0 |
| Ausstattung / Beilage: | Paperback |
| Einband: | Kartoniert / Broschiert |
| Autor: | Gad, Ahmed Fawzy |
| Auflage: | 1st ed. |
| Hersteller: |
Apress
Apress L.P. |
| Verantwortliche Person für die EU: | Books on Demand GmbH, In de Tarpen 42, D-22848 Norderstedt, info@bod.de |
| Maße: | 254 x 178 x 24 mm |
| Von/Mit: | Ahmed Fawzy Gad |
| Erscheinungsdatum: | 06.12.2018 |
| Gewicht: | 0,8 kg |
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