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Deep Learning Nanodegree Program

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Course Report - Deep Learning Nanodegree Program

Course Report

Find detailed report of this course which helps you make an informed decision on its relevance to your learning needs. Find out the course's popularity among Careervira users and the job roles that would find the course relevant for their upskilling here. You can also find how this course compares against similar courses and much more in the course report.

Course Features

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Duration

4 months

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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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Effort

10 hours per week

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Teaching Type

Self Paced

Course Description

Learn how to use PyTorch, a deep learning framework that teaches you how to create and implement neural networks. Learn how to make convolutional networks that recognize images, recurrent networks that generate sequences, and generative adversarial network for image generation accessible via a website.

Course Overview

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Job Assistance

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Personlized Teaching

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International Faculty

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Post Course Interactions

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Hands-On Training

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Case Studies, Captstone Projects

Skills You Will Gain

Prerequisites/Requirements

This program has been created specifically for students who are interested in machine learning, AI, and/or deep learning, and who have a basic working knowledge of Python programming Outside of that Python expectation, it's a very beginner-friendly progra

What You Will Learn

Convolutional Neural Networks

Deploying a Sentiment Analysis Model

Learn to build the deep learning models that are revolutionizing artificial intelligence

Neural Networks

Recurrent Neural Networks

Target Students

This Nanodegree program accepts everyone, regardless of experience and specific background

Course Instructors

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Alexis Cook

Instructor

Alexis is an applied mathematician with a Masters in computer science from Brown University and a Masters in applied mathematics from the University of Michigan. She was formerly a National Science Foundation Graduate Research Fellow.
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Cezanne Camacho

Curriculum Lead

Cezanne is a computer vision expert with a Masters in Electrical Engineering from Stanford University. As a former genomics and biomedical imaging researcher, she’s applied computer vision and deep learning to medical diagnostics.
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Daniel Jiang

Machine Learning Engineer

Daniel is a machine learning engineer who studied computer science at the University of California, Berkeley. He has worked on machine learning research at a variety of industry and academic groups.
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Jay Alammar

Instructor

Jay has a degree in computer science, loves visualizing machine learning concepts, and is the Investment Principal at STV, a $500 million venture capital fund focused on high-technology startups.
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