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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

Use cases

RNNs have several use cases. Here is a list of the most frequently used:

  • Language modelling and text generation: This is the attempt to predict the likelihood of the next word, given a sequence of words. This is useful for language translation: the most likely sentence would be the one that is correct.
  • Machine translation: This is the attempt to translate text from one language to another.
  • Anomaly detection in time series: It has been demonstrated that LSTM networks in particular are useful for learning sequences containing longer term patterns of unknown length, due to their ability to maintain long-term memory. For this reason they are useful for anomaly or fault detection in time series. Practical use cases are in log analysis and sensor data analysis.
  • Speech recognition: This is the attempt to predict phonetic segments based on input sound waves and then to formulate...

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