By Rodolfo Bonnin

Key Features

  • Bored of an excessive amount of idea on TensorFlow? This ebook is what you would like! 13 strong initiatives and 4 examples train you ways to enforce TensorFlow in production.
  • This example-rich consultant teaches you ways to accomplish hugely actual and effective numerical computing with TensorFlow
  • It is a realistic and methodically defined consultant which will observe Tensorflow’s beneficial properties from the very beginning.

Book Description

This publication of tasks highlights how TensorFlow can be utilized in numerous eventualities - this contains tasks for education versions, computing device studying, deep studying, and dealing with a number of neural networks. every one undertaking presents fascinating and insightful workouts that would educate you ways to take advantage of TensorFlow and convey you ways layers of information might be explored via operating with Tensors. easily decide a venture that's in accordance with your setting and get stacks of data on the way to enforce TensorFlow in production.

What you'll learn

  • Load, have interaction, dissect, method, and store complicated datasets
  • Solve category and regression difficulties utilizing state-of-the-art recommendations
  • Predict the end result of an easy time sequence utilizing Linear Regression modeling
  • Use a Logistic Regression scheme to foretell the longer term results of a time series
  • Classify pictures utilizing deep neural community schemes
  • Tag a suite of pictures and observe beneficial properties utilizing a deep neural community, together with a Convolutional Neural community (CNN) layer
  • Resolve personality attractiveness difficulties utilizing the Recurrent Neural community (RNN) model

About the Author

Rodolfo Bonnin is a structures engineer and PhD pupil at Universidad Tecnológica Nacional, Argentina. He additionally pursued parallel programming and photograph knowing postgraduate classes at Uni Stuttgart, Germany.

He has performed study on excessive functionality computing in view that 2005 and started learning and imposing convolutional neural networks in 2008,writing a CPU and GPU - aiding neural community feed ahead level. extra lately he is been operating within the box of fraud development detection with Neural Networks, and is at the moment engaged on sign class utilizing ML techniques.

Table of Contents

  1. Exploring and remodeling Data
  2. Clustering
  3. Linear Regression
  4. Logistic Regression
  5. Simple FeedForward Neural Networks
  6. Convolutional Neural Networks
  7. Recurrent Neural Networks and LSTM
  8. Deep Neural Networks
  9. Running types at Scale – GPU and Serving
  10. Library set up and extra Tips

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Example text

Summary In this chapter we have learned the main data structures and simple operations we can apply to data, and a succinct summary of the parts of a computational graph. They allow the data scientist to decide on simpler models if the separation of classes or the adjusting functions look sufficiently clear, or to advance directly to much more sophisticated tools, having looked at the overall characteristics of the current data. In the next chapter, we will begin building and running graphs, and will solve problems using some of the methods found in this chapter.

However, the model builder has to take into account which of the possible hundred information dimensions it should save, to later serve as an analysis tool. To save all the required information, TensorFlow API uses data output objects, called Summaries. These Summaries write results into TensorFlow event files, which gather all the required data generated during a Session's run. __init__(logdir, graph_def=None) This command will create a SummaryWriter and an event file, in the path of the parameter.

SummaryWriter. If it receives one, then TensorBoard will visualize your graph as well. Instead, consider running the merged summary op every n steps. Double-click to expand a high-level node. Sequence of numbered nodes that are not connected to each other. Sequence of numbered nodes that are connected to each other. An individual operation node. A constant. A summary node. Edge showing the data flow between operations. Edge showing the control dependency between operations. A reference edge showing that the outgoing operation node can mutate the incoming tensor.

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