Lesson 1
Welcome to the course!
Welcome to this course on deep learning with PyTorch!
Free
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
No experience required
Course Lessons
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
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.
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.
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.
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.
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.
Taught By The Best
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.
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.
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.
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.
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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Intro to Deep Learning with PyTorch