Machine learning and deep learning have been rapidly adopted in providing solutions to various real life problems. If you wish to build scalable machine learning/deep learning-powered solutions, you need to understand how to use tools to build them.

The TensorFlow is an open source machine learning framework. It enables the use of data flow graphs for numerical computations, with automatic parallelization across several CPUs, GPUs or TPUs. Its architecture makes it ideal for implementing neural networks and other machine learning/deep learning algorithms.

This tutorial will provide hands-on exposure to implement the most important and fundamental principles of machine learning and deep learning using TensorFlow.

 
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Outline/Structure of the Tutorial

  • Introduction to TensorFlow
    • Why TensorFlow?
    • TensorFlow Installation
    • TensorFlow Basic Examples
  • Basic Neural Networks using TensorFlow
  • Deep Neural Networks using TensorFlow
  • Demonstration of Deep Learning based Healthcare Application using TensorFlow
  • Future Research Directions
  • Conclusions

Learning Outcome

After attending this tutorial, participants will be able to…

  • Understand TensorFlow’s computation graph approach, elements and built-in functions.

  • Build machine learning/deep learning models using TensorFlow libraries.

  • Develop machine learning/deep learning based applications using TensorFlow.

Target Audience

Students, faculty members, researchers as well as Industrialists who are working in the field of machine learning/deep learning or wish to start building machine learning/deep learning based applications.

Prerequisites for Attendees

  • Familiarity with fundamentals of machine Learning and matrices

  • No experience with TensorFlow required

schedule Submitted 4 days ago

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