Deep Neural Networks with PyTorch

Course Cover

5

(8)

compare button icon

Course Features

icon

Duration

31 hours

icon

Delivery Method

Online

icon

Available on

Limited Access

icon

Accessibility

Desktop, Laptop

icon

Language

English

icon

Subtitles

English

icon

Level

Intermediate

icon

Teaching Type

Self Paced

icon

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

projects-img

International Faculty

projects-img

Post Course Interactions

projects-img

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

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.

Course Reviews

Average Rating Based on 8 reviews

4.9

88%

13%

Course Cover