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Tackle common commercial machine learning problems with Google’s TensorFlow library and build deployable solutions

Machine Learning with TensorFlow

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Tackle common commercial machine learning problems with Google’s TensorFlow library and build deployable solutions

  • Video Duration:02 Hours
  • Cost: $ 124.99

Course Details

TensorFlow is an open source software library for numerical computation using data flow graphs. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.

This video will approach common commercial machine learning problems using Google’s TensorFlow library. It will cover unique features of the library such as Data Flow Graphs, training, visualisation of performance with TensorBoard – all within an example-rich context using problems from multiple industries. The focus will be towards introducing new concepts through problems which are coded and solved over the course of each section.

Who all can attend

This video is for data scientists and researchers who are looking to either migrate from an existing machine learning library or jump into a machine learning platform headfirst. Also aimed at software developers who wish to learn deep learning by example. Particular focus is placed on solving commercial deep learning problems from several industries using TensorFlow’s unique features. No commercial domain knowledge is required but familiarity with Python and matrix math is expected.

What you will learn from this course

  • Setting up basic and advanced TensorFlow installations
  • Deep-diving into training, validating and monitoring training performance
  • Setting up and running cross-sectional examples (images, time-series, text, audio)
  • Creating pipelines to deal with real-world input data
  • Setting up and running cross domain-specific examples (economics, medicine, text classification, advertising)
  • Empowering the user to go from concept to a production-ready machine learning setup/pipeline capable of real-world usage

Course Content

  1. Getting Started with Deep Learning
    • The Course Overview
    • Introducing Deep Learning
    • Installing TensorFlow on Mac OS X
    • Installation on Windows – Pre-Reqeusite Virtual Machine Setup
    • Installation on Windows/Linux

  2. Your First Classifier
    • The Hand-Written Letters Dataset
    • Automating Data Preparation
    • Understanding Matrix Conversions
    • SThe Machine Learning Life Cycle
    • Reviewing Outputs and Results

  3. The TensorFlow Toolbox
    • Getting Started with TensorBoard
    • TensorBoard Events and Histograms
    • The Graph Explorer
    • Our Previous Project on TensorBoard

  4. Cats and Dogs – Convolutional Neural Networks
    • Fully Connected Neural Networks
    • Convolutional Neural Networks
    • Programming a CNN
    • Using TensorBoard on Our CNN
    • CNN Versus Fully Connected Network Performance

Contact Us

Instructor-led online training is also available for the same course.

For more details, write to us at :

Emailinfo@multisoftvirtualacademy.com

Call(+91) 8130666206/209

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