Intro to Deep Learning with PyTorch

Online Course

Intro to Deep Learning with PyTorch

What is the course about?

Intro to Deep Learning with PyTorch
The course Intro to Deep Learning with PyTorch is an online class provided by Udacity. The skill level of the course is Intermediate. It may be possible to receive a verified certification or use the course to prepare for a degree.

In this course, you’ll learn the basics of deep learning, and build your own deep neural networks using PyTorch. You’ll get practical experience with PyTorch through coding exercises and projects implementing state-of-the-art AI applications such as style transfer and text generation.

Course description
  • Intro to Deep Learning with PyTorch
  • 2 months
  • Use PyTorch to implement your first deep neural network
  • Build Deep Learning Models Today
  • Introduction to Deep Learning
  • Introduction to PyTorch
  • Deep Learning with PyTorch
  • Convolutional Neural Networks
  • Style Transfer
  • Recurrent Neural Networks
  • Natural Language Classification
  • Deploying with PyTorch
  • Discover the basic concepts of deep learning such as neural networks and gradient descent
  • Implement a neural network in NumPy and train it using gradient descent with in-class programming exercises
  • Build a neural network to predict student admissions
  • Hear from Soumith Chintala, the creator of PyTorch, how the framework came to be, where it’s being used now, and how it’s changing the future of deep learning
  • Build your first neural network with PyTorch to classify images of clothing
  • Work through a set of Jupyter Notebooks to learn the major components of PyTorch
  • Load a pre-trained neural network to build a state-of-the-art image classifier
  • Use PyTorch to build Convolutional Neural Networks for state-of-the-art computer vision applications
  • Train a convolutional network to classify dog breeds from images of dogs
  • Use a pre-trained convolutional network to create new art by merging the style of one image with the content of another image
  • Implement the paper “A Neural Algorithm of Artistic Style” by Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge”
  • Build recurrent neural networks with PyTorch that can learn from sequential data such as natural language
  • Implement a network that learns from Tolstoy’s Anna Karenina to generate new text based on the novel
  • Use PyTorch to implement a recurrent neural network that can classify text
  • Use your network to predict the sentiment of movie reviews
  • Soumith Chintala teaches you how to deploy deep learning models with PyTorch
  • Build a chatbot and compile the network for deployment in a production environment
  • To succeed in this course, you’ll need to be comfortable with Python and data processing libraries such as NumPy and Matplotlib. Basic knowledge of linear algebra and calculus is recommended, but isn’t required to complete the exercises.
  • See the Technology Requirements for using Udacity.
  • Deep learning is driving the AI revolution and PyTorch is making it easier than ever for anyone to build deep learning applications. In this course, you’ll gain practical experience building and training deep neural networks using PyTorch. You’ll be able to use these skills on your own personal projects.

Prerequisites & Facts

Intro to Deep Learning with PyTorch

Course Topic

Deep Learning

University, College, Institution


Course Skill Level


Course Language


Place of class

Online, self-paced (see curriculum for more information)



Degree & Cost

Intro to Deep Learning with PyTorch

To obtain a verified certificate from Udacity you have to finish this course or the latest version of it, if there is a new edition. The class may be free of charge, but there could be some cost to receive a verified certificate or to access the learning materials. The specifics of the course may have been changed, please consult the provider to get the latest quotes and news.
Intro to Deep Learning with PyTorch
provided by Udacity


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School: Udacity
Topic: Deep Learning