Download Building Machine Learning Projects with TensorFlow by Rodolfo Bonnin PDF

By Rodolfo Bonnin

Key Features

  • Bored of an excessive amount of thought on TensorFlow? This booklet is what you would like! 13 sturdy tasks and 4 examples educate you ways to enforce TensorFlow in production.
  • This example-rich consultant teaches you ways to accomplish hugely exact and effective numerical computing with TensorFlow
  • It is a pragmatic and methodically defined advisor for you to observe Tensorflow’s good points from the very beginning.

Book Description

This publication of tasks highlights how TensorFlow can be utilized in several situations - this contains initiatives for education types, computer studying, deep studying, and dealing with a number of neural networks. every one venture presents interesting and insightful workouts that would educate you ways to exploit TensorFlow and exhibit you the way layers of knowledge will be explored by way of operating with Tensors. easily choose a undertaking that's in keeping with your surroundings and get stacks of data on easy methods to enforce TensorFlow in production.

What you'll learn

  • Load, engage, dissect, procedure, and retailer complicated datasets
  • Solve class and regression difficulties utilizing state-of-the-art recommendations
  • Predict the 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 notice good points 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 platforms engineer and PhD scholar at Universidad Tecnológica Nacional, Argentina. He additionally pursued parallel programming and photograph realizing postgraduate classes at Uni Stuttgart, Germany.

He has performed examine on excessive functionality computing given that 2005 and commenced learning and enforcing 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 category 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 install and extra Tips

Show description

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Extra info for Building Machine Learning Projects with TensorFlow

Example text

As_graph_def(from_version=None, add_shapes=False): returns a serialized GraphDef representation of this graph. 0 } } } } ... node { name: "MatMul" op: "MatMul" input: "Placeholder" input: "Variable/read" attr { key: "T" value { type: DT_FLOAT } } ... The Session object starts empty, and when the programmer creates the different operations and tensors, they will be added automatically to the Session, which will do no computation until the Run() method is called. The Run() method takes a set of output names that need to be computed, as well as an optional set of tensors to be fed into the graph in place of certain outputs of nodes.

Eval() #Expanding dims Out[9]: array([[[ 2, 5, 3, -5]], [[ 0, 3, -2, 5]], [[ 4, 3, 5, 3]], [[ 6, 1, 4, 0]]], dtype=int32) Tensor slicing and joining In order to extract and merge useful information from big datasets, the slicing and joining methods allow you to consolidate the required column information without having to occupy memory space with nonspecific information. eval() # Reverse matrix Out[13]: array([[3, 2, 1], [6, 5, 4], [9, 8, 7]], dtype=int32) Dataflow structure and results visualization - TensorBoard Visualizing summarized information is a vital part of any data scientist's toolbox.

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. It will turn a darker color, and details about it and the nodes it connects to will appear in the info card in the upper-right corner of the visualization. Select any high-degree node, and the corresponding node icons for its other connections will be selected as well.

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