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| 008 | 181205s2018 xxu|||| o |||| 0|eng | ||
| 010 | _a 2019738255 | ||
| 020 | _a9781484241677 | ||
| 024 | 7 |
_a10.1007/978-1-4842-4167-7 _2doi |
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| 035 | _a(DE-He213)978-1-4842-4167-7 | ||
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_aDLC _beng _epn _erda _cDLC |
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_a621.367 _21 _bGADA |
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_aGad, Ahmed Fawzy. _932266 |
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| 245 | 1 | 0 |
_aPractical computer vision applications using deep learning with CNNs : _bwith detailed examples in python using tensorflow and kivy / _cBy Ahmed Fawzy Gad. |
| 250 | _a1st ed. | ||
| 260 |
_aNew York : _bApress , _c2019. |
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| 300 |
_axxii,405p. ; _c24 cm. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 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. | |
| 520 | _aDeploy 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. | ||
| 588 | _aDescription based on publisher-supplied MARC data. | ||
| 650 | 0 |
_aArtificial intelligence. _932267 |
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| 650 | 0 |
_aPython (Computer program language). _932268 |
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| 650 | 0 |
_aOpen source software. _932269 |
|
| 650 | 0 |
_aComputer programming. _932270 |
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| 650 | 1 | 4 |
_aArtificial Intelligence. _0https://scigraph.springernature.com/ontologies/product-market-codes/I21000 _932271 |
| 650 | 2 | 4 |
_aPython. _0https://scigraph.springernature.com/ontologies/product-market-codes/I29080 _932272 |
| 650 | 2 | 4 |
_aOpen Source. _0https://scigraph.springernature.com/ontologies/product-market-codes/I29090 _932273 |
| 776 | 0 | 8 |
_iPrinted edition: _z9781484241660 |
| 776 | 0 | 8 |
_iPrinted edition: _z9781484241684 |
| 776 | 0 | 8 |
_iPrinted edition: _z9781484246757 |
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