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Introduction to Deep Learning in Python

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Certification

Introduction to Deep Learning in Python

Find out the basics of neural networks, and how Keras 2.0 can be used to help you create deep learning models.

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Description

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Features

This course includes

Duration

4 hours
Video Content
4 hours
Level
Intermediate
Instruction Type
Self Paced
Delivery Method
Online
Available on
Desktop, Laptop
Accessibility
Limited Access
Language
English
Subtitles
English

Careervira Take

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Course Credibility

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Pedagogy

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Hands-on training

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Career impact

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Pricing

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Assessment

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Skills

Deep Learning ApplicationsPython ProgrammingRegression AnalysisNeural Network

Learning Goals

You'll gain hands-on, practical knowledge of how to use deep learning with Keras 2.0, the latest version of a cutting-edge library for deep learning in Python
In this chapter, you'll become familiar with the fundamental concepts and terminology used in deep learning, and understand why deep learning techniques are so powerful today
You'll use a method called backward propagation, which is one of the most important techniques in deep learning
You'll learn about the Specify-Compile-Fit workflow that you can use to make predictions

Course Content

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Prerequisites/Requirements

Understanding of Supervised Learning with scikit-learn

Instructors

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Dan Becker

Data Scientist and contributor to Keras and TensorFlow libraries

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Course Overview

Hands-On Training, Instructor-Moderated Discussions

Post course interactions

Virtual labs

International faculty

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