Practical Computer Vision Applications Using Deep Learning with CNNs
Apress (Verlag)
9781484241660 (ISBN)
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.
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.
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.
| Erscheinungsdatum | 21.12.2018 |
|---|---|
| Zusatzinfo | 200 Illustrations, black and white; XXII, 405 p. 200 illus. |
| Verlagsort | Berkley |
| Sprache | englisch |
| Maße | 178 x 254 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge |
| Mathematik / Informatik ► Informatik ► Software Entwicklung | |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Schlagworte | computer vision • convolutional neural network • Deep learning • Image Processing • machine learning • neural network • Python • tensorflow |
| ISBN-13 | 9781484241660 / 9781484241660 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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