Free Course

Intro to Deep Learning with PyTorch

by
Facebook AI

Use PyTorch to implement your first deep neural network

Nanodegree Program

Deep Learning

Build cutting-edge AI projects supported by dedicated mentors.

About this Course

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 Cost
Free
Timeline
Approx. 2 month
Skill Level
intermediate
Included in Product

Rich Learning Content

Taught by Industry Pros

Interactive Quizzes

Self-Paced Learning

Join the Path to Greatness

With the skills you learn in this course, you’ll be ready to take on the complex challenges in our Deep Learning Nanodegree program, as you build towards a career in data science or machine learning.

Free Course

Intro to Deep Learning with PyTorch

byFacebook AI

Learn the basics of deep learning and implement your own deep neural networks with PyTorch.

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Course Leads

Cezanne Camacho

Cezanne Camacho

Lead Instructor

Soumith Chintala

Soumith Chintala

Instructor

Luis Serrano

Luis Serrano

Instructor

Mat Leonard

Mat Leonard

Content Developer

What You Will Learn

lesson 1

Introduction to Deep Learning

  • 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
lesson 2

Introduction to PyTorch

  • 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
lesson 3

Deep Learning with PyTorch

  • 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
lesson 4

Convolutional Neural Networks

  • 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
lesson 5

Style Transfer

  • 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"
lesson 6

Recurrent Neural Networks

  • 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
lesson 7

Natural Language Classification

  • Use PyTorch to implement a recurrent neural network that can classify text
  • Use your network to predict the sentiment of movie reviews
lesson 8

Deploying with PyTorch

  • 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

Prerequisites and Requirements

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.

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Free Courses

Rich Learning Content

Interactive Quizzes

Self Paced Learning

Taught by Industry Professionals

 

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Why Take This Course

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.

What do I get?
Instructor videosLearn by doing exercisesTaught by industry professionals

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