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Hands-On Deep Learning with Apache Spark

Hands-On Deep Learning with Apache Spark

By : Iozzia
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Hands-On Deep Learning with Apache Spark

Hands-On Deep Learning with Apache Spark

By: Iozzia

Overview of this book

Deep learning is a subset of machine learning where datasets with several layers of complexity can be processed. Hands-On Deep Learning with Apache Spark addresses the sheer complexity of technical and analytical parts and the speed at which deep learning solutions can be implemented on Apache Spark. The book starts with the fundamentals of Apache Spark and deep learning. You will set up Spark for deep learning, learn principles of distributed modeling, and understand different types of neural nets. You will then implement deep learning models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) on Spark. As you progress through the book, you will gain hands-on experience of what it takes to understand the complex datasets you are dealing with. During the course of this book, you will use popular deep learning frameworks, such as TensorFlow, Deeplearning4j, and Keras to train your distributed models. By the end of this book, you'll have gained experience with the implementation of your models on a variety of use cases.
Table of Contents (19 chapters)
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Appendix A: Functional Programming in Scala
Appendix B: Image Data Preparation for Spark

Implementing an end-to-end image classification web application

Using all of the things that we learned about in the previous chapters of this book, we should now be able to implement a real-world web application that allows users to upload an image and then properly classify it.

Picking up a proper Keras model

We are going to use an existing, pre-trained Python Keras CNN model. Keras applications (https://keras.io/applications/) are a set of DL models that are available as part of the framework with pre-trained weights. Among those models is VGG16, a 16-layer CNN that was implemented by the Visual Geometry Group at the University of Oxford in 2014. This model is compatible with a TensorFlow backend. It has been trained on...

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