Deep Learning with Python and PyTorch

Overview

In order to be successful in completing this course, please ensure you are familiar with PyTorch Basics and have practical knowledge to apply it to Machine Learning. If you do not have this pre-requiste knowledge, it is highly recommended you complete the PyTorch Basics for Machine Learning course prior to starting this course.

This Deep Learning with Python and PyTorch course offered by EdX in partnership with IBMx is the second part of a two-part course on how to develop Deep Learning models using Pytorch.

In the first course, you learned the basics of PyTorch; in this course, you will learn how to build deep neural networks in PyTorch. Also, you will learn how to train these models using state of the art methods. 

You will first review multiclass classification, learning how to build and train a multiclass linear classifier in PyTorch. 

This will be followed by an in-depth introduction on how to construct Feed-forward neural networks in PyTorch, learning how to train these models, how to adjust hyperparameters such as activation functions and the number of neurons.

What you'll learn

• Apply knowledge of Deep Neural Networks and related machine learning methods

• Build and Train Deep Neural Networks using PyTorch

• Build Deep learning pipelines