Tensor Flow

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About

Tensor Flow is an all-encompassing open-source Machine Learning (ML) platform. It empowers developers in creating applications involving Deep Learning, besides being crucial for training and inferential analysis of Deep Neural Networks. It is a comprehensive and flexible system constituted of tools, libraries, and community-based resources.

Tensor Flow has the capacity to handle vast amounts of data through its higher dimension and multi-dimensional arrays called Tensors. Data Flow Graphs enable distributed code execution across a cluster of systems.

  • Easy Debugging
    Tensor Flow comes with an Eager Execution mode that lends efficiency to debugging process. It allows the operations immediate execution instead of waiting for the computational graph stage. It offers the developer to debug immediately and at each step inducing transparency to the process.
  • Faster Execution
    You can distribute computation across systems by choosing a distribution strategy that suits your needs. It helps in the faster execution of complex Tensor Flow models, especially those involving training, inference, and evaluation.
  • Minimizing Errors
    Tensor Flow brings you the advantage of special Loss Functions (Cost Functions) that help in minimizing the error between the expected and actual output. There is a variety of losses used depending upon the datasets, Tensor Flow models, and performance. Examples of Loss Functions include Binary Cross-Entropy, Poisson, Hinge, Means Squared, and Kullback-Leibler Divergence, etc.

You can subscribe Nagios Core to an AWS Marketplace product and launch an instance from the Nagios Core product’s AMI using the Amazon EC2 launch wizard.

To launch an instance from the AWS Marketplace using the launch wizard

  • Open the Amazon EC2 console at https://console.aws.amazon.com/ec2/
  • From the Amazon EC2 dashboard, choose Launch Instance. On the Choose an Amazon Machine Image (AMI) page, choose the AWS Marketplace category on the left. Find a suitable AMI by browsing the categories, or using the search functionality. Choose Select to choose your product.
  • A dialog displays an overview of the product you’ve selected. You can view the pricing information, as well as any other information that the vendor has provided. When you’re ready, choose Continue.
  • On the Choose an Instance Type page, select the hardware configuration and size of the instance to launch. When you’re done, choose Next: Configure Instance Details.
  • On the next pages of the wizard, you can configure your instance, add storage, and add tags. For more information about the different options you can configure, see Launching an Instance. Choose Next until you reach the Configure Security Group page.
  • The wizard creates a new security group according to the vendor’s specifications for the product. The security group may include rules that allow all IP addresses (0.0.0.0/0) access on SSH (port 22) on Linux or RDP (port 3389) on Windows. We recommend that you adjust these rules to allow only a specific address or range of addresses to access your instance over those ports
  • When you are ready, choose Review and Launch.
  • On the Review Instance Launch page, check the details of the AMI from which you’re about to launch the instance, as well as the other configuration details you set up in the wizard. When you’re ready, choose Launch to select or create a key pair, and launch your instance.
  • Depending on the product you’ve subscribed to, the instance may take a few minutes or more to launch. You are first subscribed to the product before your instance can launch. If there are any problems with your credit card details, you will be asked to update your account details. When the launch confirmation page displays.

Usage / Deployment Instructions

To access the application:

Step 1: ssh into EC2 machine using putty or terminal with public ip and the key that you have generated.

Step 2:  Creating a Virtual Environment

$ mkdir my_tensorflow

$ cd my_tensorflow

Step 3: python3 -m venv venv

Step 4: $  source venv/bin/activate

Step 5: $ pip install --upgrade pip

Step 6: $  pip install --upgrade tensorflow

Step 7: $ python -c 'import tensorflow as tf; print(tf.__version__)'

Step 8: $ deactivate

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    The Apache Hadoop software library allows for the distributed processing of large data sets across clusters of computers using a simple programming model. The software library is designed to scale from single servers to thousands of machines; each server using local computation and storage. Instead of relying on hardware to deliver high-availability, the library itself handles failures at the application layer. As a result, the impact of failures is minimized by delivering a highly-available service on top of a cluster of computers.

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    Front-end use of AI technologies to enable Intelligent Assistants for customer care is certainly key, but there are many other applications. One that I think is particularly interesting is the application of AI to directly support — rather than replace — contact center agents. Technologies such as natural language understanding and speech recognition can be used live during a customer service interaction with a human agent to look up relevant information and make suggestions about how to respond. AI technologies also have an important role in analytics. They can be used to provide an overview of activities within a call center, in addition to providing valuable business insights from customer activity.

    There are many machine learning algorithms in use today, but the most popular ones are:

    • Decision Trees
    • Naive Bayes Classification
    • Ordinary Least Squares Regression
    • Logistic Regression
    • Support vector machines
    • Ensemble Methods
    • Clustering Algorithms
    • Principal Component Analysis
    • Singular Value Decomposition
    • Independent Component Analysis

    Highlights

    • icon

      Tensor Flow is usable with Python, Javascript, Java, and C++.

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      It supports both CPUs and GPUs. GPUs are especially handy in developing deep learning applications.

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      Developers can choose to deploy models across a variety of platforms such as servers, cloud, mobile, and edge devices, browsers, and a range of Javascript platforms.

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      The flexible architecture of Tensor Flow makes the development and publication of complex models easy. From conceptualization to execution!

    Application Installed

    • icon Tensor Flow
    • icon linux
    • icon Python