Deep Neural Networks with PyTorch

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Course Features

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Duration

31 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

31 hours

Course Description

This course will show you how to create deep learning models with Pytorch. The course will begin with Pytorch's Tensors and Automatic differentiation packages. Each section will then cover different models, starting with Linear Regression and logistic/softmax. Then comes Feedforward deep neural network, which will cover the roles of different activation functions and normalization layers. Convolutional neural networks and Transfer learning will then be covered. Several other Deep learning methods are also covered.

Learning Outcomes

Course Overview

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International Faculty

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Post Course Interactions

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Instructor-Moderated Discussions

Skills You Will Gain

What You Will Learn

You will learn how to develop deep learning models using Pytorch

Knowledge of Deep Neural Networks and related machine learning methods

Course Instructors

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Joseph Santarcangelo

Ph.D., Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

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