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Big Data Architect???s Handbook

Big Data Architect???s Handbook

By : Akhtar
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Big Data Architect???s Handbook

Big Data Architect???s Handbook

3 (2)
By: Akhtar

Overview of this book

The big data architects are the “masters” of data, and hold high value in today’s market. Handling big data, be it of good or bad quality, is not an easy task. The prime job for any big data architect is to build an end-to-end big data solution that integrates data from different sources and analyzes it to find useful, hidden insights. Big Data Architect’s Handbook takes you through developing a complete, end-to-end big data pipeline, which will lay the foundation for you and provide the necessary knowledge required to be an architect in big data. Right from understanding the design considerations to implementing a solid, efficient, and scalable data pipeline, this book walks you through all the essential aspects of big data. It also gives you an overview of how you can leverage the power of various big data tools such as Apache Hadoop and ElasticSearch in order to bring them together and build an efficient big data solution. By the end of this book, you will be able to build your own design system which integrates, maintains, visualizes, and monitors your data. In addition, you will have a smooth design flow in each process, putting insights in action.
Table of Contents (21 chapters)
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10
Frontend Architecture
17
Data Visualization

Deep learning using TensorFlow

Nowadays, deep learning is a very hot topic. We have already established an understanding about what deep learning is in the previous chapter. Before proceeding further, let's recap a little bit. Deep learning is a branch of machine learning that is based on neural networks similar to a human brain; more specifically, a neural network with a number of hidden layers is called a deep neural network or deep learning. The more hidden layers, the deeper the network and learning will be.

There are many open source libraries and frameworks available that can handle the complex computation involved in designing a neural network. Some of them are listed as follows: 

  • TensorFlow
  • Caffe
  • Theano
  • Keras
  • Apache MXNet
  • Torch
  • Microsoft Cognitive Toolkit 
  • Pytorch
  • Deeplearning4j
  • Lasagne

We will be going through the TensorFlow framework in the subsequent...

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