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Hands-On Deep Learning for Images with TensorFlow : Build Intelligent Computer Vision Applications Using TensorFlow and Keras.

Chapter 4: A Convolutional Neural Network; Convolutions; Pooling; Building a convolutional neural network; Deep neural network; Summary; Chapter 5: An Image Classification Server; REST API definition; Trained models in Docker containers; Making predictions; Summary; Other Books You May Enjoy; Index.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Ballard, Will
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham : Packt Publishing Ltd, 2018.
Temas:
Acceso en línea:Texto completo

MARC

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245 1 0 |a Hands-On Deep Learning for Images with TensorFlow :  |b Build Intelligent Computer Vision Applications Using TensorFlow and Keras. 
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300 |a 1 online resource (92 pages) 
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505 0 |a Cover; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Machine Learning Toolkit; Installing Docker; The machine learning Docker file; Sharing data; Machine learning REST service; Summary; Chapter 2: Image Data; MNIST digits; Tensors -- multidimensional arrays; Turning images into tensors; Turning categories into tensors; Summary; Chapter 3: Classical Neural Network; Comparison between classical dense neural networks; Activation and nonlinearity; Softmax; Training and testing data; Dropout and Flatten; Solvers; Hyperparameters; Grid searches. 
520 |a Chapter 4: A Convolutional Neural Network; Convolutions; Pooling; Building a convolutional neural network; Deep neural network; Summary; Chapter 5: An Image Classification Server; REST API definition; Trained models in Docker containers; Making predictions; Summary; Other Books You May Enjoy; Index. 
520 |a In this book, you will come across various real-world projects which will teach you how to leverage Tensforflow's capabilities to perform efficient image processing tasks. By the end of this book, you will have mastered all the concepts of deep learning for Images and their implementations with Tensorflow and Keras. 
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