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Introduction to Deep Learning in Python

Course Cover

5

(3)

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Course Features

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Duration

4 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

Beginner

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Teaching Type

Self Paced

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Video Content

4 hours

Course Description

Deep learning is a powerful machine-learning technique that has revolutionized various fields, including robotics and natural language processing. One example of its potential is seen in AlphaGo. For individuals interested in exploring this exciting technology, this course offers a hands-on experience in deep learning using Keras 2.0. Keras 2.0 is the latest version of a Python library specifically designed for deep learning. By enrolling in this course, participants will gain practical knowledge and skills in deep machine learning, as well as the ability to use Python for machine learning purposes. The course will cover the fundamentals of neural networks in machine learning using Python and introduce learners to important algorithms used in deep learning. Whether you are new to machine learning or already have some background, this course provides a comprehensive introduction to the field and offers opportunities for further growth and development. By the end of the course, participants will have a solid understanding of machine learning concepts and be able to apply them using Python programming. This course is perfect for those who want to learn about both machine learning and deep learning, providing a solid foundation for future studies or career opportunities in these areas. So don't miss out on this chance to jumpstart your journey into the exciting world of machine learning using Python!

Course Overview

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Virtual Labs

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International Faculty

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Post Course Interactions

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Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

Understanding of Supervised Learning with scikit-learn

What You Will Learn

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 Instructors

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Dan Becker

Data Scientist and contributor to Keras and TensorFlow libraries

Dan Becker is a data scientist with years of deep learning experience. He has contributed to the Keras and TensorFlow libraries, finishing 2nd (out of 1353 teams) in the $3million Heritage Health Pri...

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