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Building Deep Learning Models with TensorFlow

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Course Features

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Duration

13 hours

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Teaching Type

Self Paced

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Video Content

13 hours

Course Description

Most of the data in the world are unlabeled or unstructured. Deep neural networks are unable to capture the relevant structure, such as images, sounds, and textual data. These types of data are best suited for deep networks, which can discover hidden structures. This course will teach you how to use TensorFlow library for deep learning on different data types to solve real-world problems.

Learning Outcomes TensorFlow is used for classification, regression, classification, and minimization error functions. Learn about the different types of Deep Architectures such as Convolutional, Recurrent, and Autoencoders. TensorFlow is used to adjust the biases and weights of the Neural Networks being trained.

Course Overview

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International Faculty

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Instructor-Moderated Discussions

Skills You Will Gain

What You Will Learn

Fundamentals of Deep Learning

You will learn about TensorFlow, and use it to create Linear and Logistic Regression models

You will learn about about Convolutional Neural Networks, and the building blocks of a convolutional neural network, such as convolution and feature learning

You will learn about the applications of unsupervised learning

You will learn about the recurrent neural network model

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