Nanodegree Program

Deep Learning

Build Deep Learning Models Today

Deep learning is driving advances in artificial intelligence that are changing our world. Enroll now to build and apply your own deep neural networks to challenges like image classification and generation, time-series prediction, and model deployment.

Enrollment Closing In

In Collaboration With
  • Amazon Web Services
  • Facebook Artificial Intelligence

Why Take This Program?

In this program, you’ll cover Convolutional and Recurrent Neural Networks, Generative Adversarial Networks, Deployment, and more. You’ll use PyTorch, and have access to GPUs to train models faster. You'll learn from authorities like Sebastian Thrun, Ian Goodfellow, Jun-Yan Zhu, and Andrew Trask. This is the ideal point-of-entry into the field of AI.


Why Take This Program?

The share of jobs requiring AI skills has grown 4.5x since 2013

Expert Instructors
Expert Instructors

Expert Instructors

Learn practical skills taught by deep learning experts including Sebastian Thrun, Ian Goodfellow, Jun-Yan-Zhu, Andrew Trask, and the Udacity Deep Learning Team.

Unique Projects, Personalized Feedback

Unique Projects, Personalized Feedback

Work on five specially-designed deep learning projects, and receive detailed feedback on each from our mentors.

Deploy Your Own Sentiment Analysis Model
Deploy Your Own Sentiment Analysis Model

Deploy Your Own Sentiment Analysis Model

You’ll get hands-on experience deploying and monitoring a model using PyTorch and Amazon SageMaker. By teaching these essential skills, we are preparing our students to be indispensable members of AI product teams.

Guaranteed Admission

Guaranteed Admission

Successfully complete the program, and receive guaranteed admission to either our Self-Driving Car Engineer or Flying Car Nanodegree programs.

Guaranteed Admission

As a graduate, you earn guaranteed admission into one of two other Nanodegree program. You’ll continue to explore even more deep learning projects alongside groundbreaking new curriculum built with our pioneering industry collaborators. Note that we recommend some C++ knowledge to get the most out of these programs.

Step 1

Enroll in the Deep Learning Nanodegree program

Step 2

Graduate within 4 months

Step 3

Enroll in one of two advanced Nanodegree programs with guaranteed admission

What You Will Learn

Download Syllabus
Syllabus

Deep Learning

Become an expert in neural networks, and learn to implement them using the deep learning framework PyTorch. Build convolutional networks for image recognition, recurrent networks for sequence generation, generative adversarial networks for image generation, and learn how to deploy models accessible from a website.

Master building and implementing neural networks for image recognition, sequence generation, image generation, and more.

See fewer details

4 months to complete

Prerequisite Knowledge

This program has been created specifically for students who are interested in machine learning, AI, and/or deep learning, and who have a working knowledge of Python programming, including NumPy and pandas. Outside of that Python expectation and some familiarity with calculus and linear algebra, it's a beginner-friendly program.See detailed requirements.

  • Introduction

    Get your first taste of deep learning by applying style transfer to your own images, and gain experience using development tools such as Anaconda and Jupyter notebooks.

  • Neural Networks

    Learn neural networks basics, and build your first network with Python and NumPy. Use the modern deep learning framework PyTorch to build multi-layer neural networks, and analyze real data.

    Predicting Bike-Sharing Patterns
  • Convolutional Neural Networks

    Learn how to build convolutional networks and use them to classify images (faces, melanomas, etc.) based on patterns and objects that appear in them. Use these networks to learn data compression and image denoising.

    Dog-Breed Classifier
  • Recurrent Neural Networks

    Build your own recurrent networks and long short-term memory networks with PyTorch; perform sentiment analysis and use recurrent networks to generate new text from TV scripts.

    Generate TV scripts
  • Generative Adversarial Networks

    Learn to understand and implement a Deep Convolutional GAN (generative adversarial network) to generate realistic images, with Ian Goodfellow, the inventor of GANs, and Jun-Yan Zhu, the creator of CycleGANs.

    Generate Faces
  • Deploying a Sentiment Analysis Model

    Train and deploy your own PyTorch sentiment analysis model. Deployment gives you the ability to use a trained model to analyze new, user input. Build a model, deploy it, and create a gateway for accessing it from a website.

    Deploying a Sentiment Analysis Model
Just as machines made human muscles a thousand times stronger, machines will make the human brain a thousand times more powerful.
— SEBASTIAN THRUN, UDACITY

In Collaboration with Top Industry Experts

Sebastian Thrun
Sebastian Thrun
Founder, Google X, Self-Driving Car Pioneer
Ian Goodfellow
Ian Goodfellow
Inventor of GANs, Author of Deep Learning (MIT Press)
Jun-Yan Zhu
Jun-Yan Zhu
Researcher at MIT CSAIL and coauthor of CycleGAN
Andrew Trask
Andrew Trask
Author of Grokking Deep Learning, Google DeepMind Scholar

Learn with the best

Mat Leonard
Mat Leonard

Product Lead

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

Luis Serrano
Luis Serrano

Head of Content

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.

Cezanne Camacho
Cezanne Camacho

Curriculum Lead

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

Alexis Cook
Alexis Cook

Instructor

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.

Jennifer Staab
Jennifer Staab

Instructor

Jennifer has a PhD in Computer Science, Masters in Biostatistics, and was a professor at Florida Polytechnic University. She previously worked at RTI International and United Therapeutics as a statistician and computer scientist.

Sean Carrell
Sean Carrell

Instructor

Sean Carrell is a former research mathematician specializing in Algebraic Combinatorics. He completed his PhD and Postdoctoral Fellowship at the University of Waterloo, Canada.

Ortal Arel
Ortal Arel

Instructor

Ortal Arel is a former computer engineering professor. She holds a Ph.D. in Computer Engineering from the University of Tennessee. Her doctoral research work was in the area of applied cryptography.

Jay Alammar
Jay Alammar

Instructor

Jay is a software engineer, the founder of Qaym (an Arabic-language review site), and the Investment Principal at STV, a $500 million venture capital fund focused on high-technology startups.

Student Reviews

4.6

(1748)

5 stars
1326
75.9%
4 stars
283
16.2%
3 stars
75
4.3%
2 stars
19
1.1%
1 stars
45
2.6%
Eigenvalue l.

Very useful content and interesting projects

Andrew B.

Excellent content and projects!

Farhan Z.

yes it is quite comprehensive in every way. Especially liked the extra material on Attention based models and a comprehensive view of GANs. The material on LSTM internal structure was excellent and the best description of LSTM gating structure I could find anywhere. Great Job!!!

Swaminathan S.

It is a great foundation for Deep learning Models and its architecture. Each section in course are designed with well thought process, completing each section properly will give great clarity to do the respective projects at each section.

Greg B.

This program was great. Covered all the basics and some more sophisticated models.

Nanodegree program
Deep Learning
$999 USD

total

Learn to build the deep learning models that are revolutionizing artificial intelligence.

Deep Learning

Build Deep Learning Models Today