Practical Computer Vision Applications Using Deep Learning with CNNs With Detailed Examples in Python Using TensorFlow and Kivy /
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...
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| Médium: | Elektronický zdroj E-kniha |
| Jazyk: | angličtina |
| Vydáno: |
Berkeley, CA :
Apress,
2018.
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| Vydání: | 1st ed. 2018. |
| Témata: | |
| ISBN: | 9781484241677 |
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| 020 | |a 9781484241677 | ||
| 024 | 7 | |a 10.1007/978-1-4842-4167-7 |2 doi | |
| 035 | |a CVTIDW12890 | ||
| 040 | |a Springer-Nature |b eng |c CVTISR |e AACR2 | ||
| 041 | |a eng | ||
| 100 | 1 | |a Gad, Ahmed Fawzy. |4 aut | |
| 245 | 1 | 0 | |a Practical Computer Vision Applications Using Deep Learning with CNNs |h [electronic resource] : |b With Detailed Examples in Python Using TensorFlow and Kivy / |c by Ahmed Fawzy Gad. |
| 250 | |a 1st ed. 2018. | ||
| 260 | 1 | |a Berkeley, CA : |b Apress, |c 2018. | |
| 300 | |a XXII, 405 p. 200 illus. |b online resource. | ||
| 500 | |a Professional and Applied Computing | ||
| 505 | 0 | |a 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. | |
| 516 | |a text file PDF | ||
| 520 | |a 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 fully connected networks. 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 application and make the pre-trained models accessible over the Internet using Flask. 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. You will: 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. | ||
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Python (Computer program language). | |
| 650 | 0 | |a Open source software. | |
| 650 | 0 | |a Computer programming. | |
| 856 | 4 | 0 | |u http://hanproxy.cvtisr.sk/han/cvti-ebook-springer-eisbn-978-1-4842-4167-7 |y Vzdialený prístup pre registrovaných používateľov |
| 910 | |b ZE10170 | ||
| 919 | |a 978-1-4842-4167-7 | ||
| 974 | |a andrea.lebedova |f Elektronické zdroje | ||
| 992 | |a SUD | ||
| 999 | |c 239709 |d 239709 | ||