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Intro to Deep Learning with PyTorch

Course

Learn the basics of deep learning, and build your own deep neural networks using PyTorch, an open source machine learning library used for applications such as NLP and Computer Vision.

Learn the basics of deep learning, and build your own deep neural networks using PyTorch, an open source machine learning library used for applications such as NLP and Computer Vision.

Last Updated March 7, 2022

Prerequisites:

No experience required

Course Lessons

Lesson 1

Welcome to the course!

Welcome to this course on deep learning with PyTorch!

Lesson 2

Introduction to Neural Networks

Learn the concepts behind how neural networks operate and how we train them using data.

Lesson 3

Talking PyTorch with Soumith Chintala

Hear from Soumith Chintala, the creator of PyTorch, about the past, present, and future of the PyTorch framework.

Lesson 4

Introduction to PyTorch

Learn how to use PyTorch to build and train deep neural networks. By the end of this lesson, you will build a network that can classify images of dogs and cats with state-of-the-art performance.

Lesson 5

Convolutional Neural Networks

Learn how to use convolutional neural networks to build state-of-the-art computer vision models.

Lesson 6

Style Transfer

Use a deep neural network to transfer the artistic style of one image onto another image.

Lesson 7

Recurrent Neural Networks

Learn how to use recurrent neural networks to learn from sequential data such as text. Build a network that can generate realistic text one letter at a time.

Lesson 8

Sentiment Prediction RNNs

Here you'll build a recurrent neural network that can accurately predict the sentiment of movie reviews.

Lesson 9

Deploying PyTorch Models

In this lesson, we'll walk through a tutorial showing how to deploy PyTorch models with Torch Script.

Taught By The Best

Photo of Luis Serrano

Luis Serrano

Instructor

Luis was formerly a Machine Learning Engineer at Google. He holds a PhD in mathematics from the University of Michigan, and a Postdoctoral Fellowship at the University of Quebec at Montreal.

Photo of Alexis Cook

Alexis Cook

Curriculum Lead

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.

Photo of Soumith Chintala

Soumith Chintala

Instructor

Soumith has worked on advanced Artificial Intelligence algorithms, and on open-source software for machine learning. He's written several open source projects and built frontend web software.

Photo of Cezanne Camacho

Cezanne Camacho

Curriculum Lead

Cezanne is an expert in computer vision with a Masters in Electrical Engineering from Stanford University. As a former researcher in genomics and biomedical imaging, she's applied computer vision and deep learning to medical diagnostic applications.

Photo of Mat Leonard

Mat Leonard

Content Developer

Mat is a former physicist, research neuroscientist, and data scientist. He did his PhD and Postdoctoral Fellowship at the University of California, Berkeley.

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