03790cam a22005055i 4500 21661673 OSt 20220504153021.0 m |o d | cr ||||||||||| 181205s2018 xxu|||| o |||| 0|eng 2019738255 9781484241677 10.1007/978-1-4842-4167-7 doi (DE-He213)978-1-4842-4167-7 DLC eng pn rda DLC UYQ bicssc COM004000 bisacsh UYQ thema 621.367 1 GADA Gad, Ahmed Fawzy. 32266 Practical computer vision applications using deep learning with CNNs : with detailed examples in python using tensorflow and kivy / By Ahmed Fawzy Gad. 1st ed. New York : Apress , 2019. xxii,405p. ; 24 cm. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda 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. 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. Description based on publisher-supplied MARC data. Artificial intelligence. 32267 Python (Computer program language). 32268 Open source software. 32269 Computer programming. 32270 Artificial Intelligence. https://scigraph.springernature.com/ontologies/product-market-codes/I21000 32271 Python. https://scigraph.springernature.com/ontologies/product-market-codes/I29080 32272 Open Source. https://scigraph.springernature.com/ontologies/product-market-codes/I29090 32273 Printed edition: 9781484241660 Printed edition: 9781484241684 Printed edition: 9781484246757 0 ibc origres u ncip 20 y-gencatlg ddc BK 1st 621.367 GADA 222663 222663 0 0 ddc 0 0 MCA AIMIT AIMIT 2022-03-24 Biblios Book Point 1199.00 Bill no:6623; Bill dt:2022-03-23 1 621.367 GADA MCA17066 2025-07-21 00:00:00 2022-05-04 959.20 2022-04-27 BK