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Building Deep Learning Models with TensorFlow

Created by -

Samaya Madhavan,JEREMY NILMEIER,Romeo Kienzler,Alex Aklson
,
IBM

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English

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Overview

The majority of data in the world is unlabeled and unstructured. Shallow neural networks cannot easily capture relevant structure in, for instance, images, sound, and textual data. Deep networks are capable of discovering hidden structures within this type of data. In this course you’ll use TensorFlow library to apply deep learning to different data types in order to solve real world problems. Learning Outcomes: After completing this course, learners will be able to: • explain foundational TensorFlow concepts such as the main functions, operations and the execution pipelines. • describe how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. • understand different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders. • apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained.

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USD 50

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Type: Online

This course includes

  • Approx. 21 hours to complete
  • Earn a Certificate upon completion
  • Start instantly and learn at your own schedule.

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course image

USD 50

provider image

Type: Online

This course includes

  • Approx. 21 hours to complete
  • Earn a Certificate upon completion
  • Start instantly and learn at your own schedule.

Taken this course?

Share your experience with other students

Share

Add Review

Building Deep Learning Models with TensorFlow

Created by -

Samaya Madhavan,JEREMY NILMEIER,Romeo Kienzler,Alex Aklson
,
IBM

0.00

(0 ratings)

All Levels

Start Date: February 10th 2021

Course Description

The majority of data in the world is unlabeled and unstructured. Shallow neural networks cannot easily capture relevant structure in, for instance, images, sound, and textual data. Deep networks are capable of discovering hidden structures within this type of data. In this course you’ll use TensorFlow library to apply deep learning to different data types in order to solve real world problems. Learning Outcomes: After completing this course, learners will be able to: • explain foundational TensorFlow concepts such as the main functions, operations and the execution pipelines. • describe how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. • understand different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders. • apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained.

The information used on this page is how each course is described on the Coursera platform.

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About the Instructor

Samaya Madhavan,JEREMY NILMEIER,Romeo Kienzler,Alex Aklson,IBM

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