Building Your First PyTorch Solution

This course covers the important practical aspects of installing PyTorch from scratch on a variety of different platforms and getting going with classification and regression models. 
Course info
Rating
(24)
Level
Beginner
Updated
Jun 28, 2019
Duration
2h 24m
Table of contents
Course Overview
Installing PyTorch on a Local Machine
Understanding Linear Regression with a Single Neuron
Building a Regression Model Using PyTorch
Building a Classification Model Using PyTorch
Description
Course info
Rating
(24)
Level
Beginner
Updated
Jun 28, 2019
Duration
2h 24m
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Description

PyTorch is fast emerging as a popular choice for building deep learning models owing to its flexibility, ease-of-use, and built-in support for optimized hardware such as GPUs. Using PyTorch, you can build complex deep learning models, while still using Python-native support for debugging and visualization.

In this course, Building Your First PyTorch Solution, you will gain the ability to get up and running by building your first regression and classification models.

First, you will learn how to install PyTorch using pip and conda, and see how to leverage GPU support. Next, you will discover how to hand-craft a linear regression model using a single neuron, by defining the loss function yourself. You will then see how PyTorch optimizers can be used to make this process a lot more seamless.

You will understand how different activation functions and dropout can be added to PyTorch neural networks. Finally, you will explore how to build classification models in PyTorch.

You will round out the course by extending the PyTorch base module to implement a custom classifier.

When you’re finished with this course, you will have the skills and knowledge to move on to installing PyTorch from scratch in a new environment and building models leveraging and customizing various PyTorch abstractions.

About the author
About the author

A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
(Music) Hi, my name is Janani Ravi, and welcome to this course on Building Your First PyTorch solution. A little about myself. I have a master's degree in electrical engineering from Stanford and have worked at companies such as Microsoft, Google, and Flipkart. At Google, I was one of the first engineers working on real-time collaborative editing in Google Docs, and I hold four patents for its underlying technologies. I currently work on my own startup, Loonycorn, a studio for high-quality video content. PyTorch is fast emerging as a popular choice for building deep learning models owing to its flexibility, ease of use, and built-in support for optimized hardware such as GPUs. Using PyTorch, you can build complex deep learning models while still using Python native support for debugging and visualization. In this course, you will gain the ability to get up and running by building your first regression and classification modules in PyTorch. First, you will learn how to install PyTorch using pip and conda and leverage GPU support. Next, you will discover how to hand craft a linear regression model using a single neuron by defining the loss function yourself. You will then see how PyTorch optimizers can be used to make this process a lot more seamless. You will then understand how different activation functions and dropout can be added to PyTorch neural networks. You will round out the course by extending the PyTorch base module to implement a custom classifier. When you are finished with this course, you will have the skills and knowledge to move on to installing PyTorch from scratch in a new environment and building modules, leveraging, and customizing various PyTorch abstractions.