In Collaboration With
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.
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
Work on five specially-designed deep learning projects, and receive detailed feedback on each from our mentors.
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
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.
Enroll in the Deep Learning Nanodegree program
Graduate within 4 months
Enroll in one of two advanced Nanodegree programs with guaranteed admission
What You Will Learn
Download SyllabusSyllabus
Deep Learning
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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 PatternsConvolutional 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 ClassifierRecurrent 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 scriptsGenerative 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 FacesDeploying 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
Founder, Google X, Self-Driving Car Pioneer
Ian Goodfellow
Inventor of GANs, Author of Deep Learning (MIT Press)
Jun-Yan Zhu
Researcher at MIT CSAIL and coauthor of CycleGAN
Andrew Trask
Author of Grokking Deep Learning, Google DeepMind Scholar
Learn with the best
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
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
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
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
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
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
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
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
Top Reviews
(1748)
5 stars
75.9%
4 stars
16.2%
3 stars
4.3%
2 stars
1.1%
1 stars
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.
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