Time
1 Four-Month Term
Study 12 hrs/week and complete in 4 mo.
Classroom Opens
January 15, 2019
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
(1707)
5 stars
75.6%
4 stars
16.4%
3 stars
4.4%
2 stars
1.1%
1 stars
2.5%
Abhilash P.
Had wonderful experience with interesting set of projects to work on! This program has helped to understand the fundamentals and as well to work on amazing projects with great feedback anf insights from the project reviewers.
Archit G.
Excellent Program
KSHITIJ K.
This is awesome. I really learned and enjoyed a lot. The topic a covered very nicely and made it easy to follow. I really don't have any doubt regarding joining to course and it really gave me an all-round knowledge of the field. Now I am heading towards more advanced area.
Giulliano M.
This course is far the best and strongest concept and practical vision about deep learning on the internet.
Meet P.
It was a great experience. All the topics have been taught in details along with mini project implementations of the topics. The assignment projects at the end of each module has boast my confidence.
Learn now, pay later
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Pay your monthly bill using a bank transfer, check, or debit card.
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Nanodegree program
Deep Learning
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Program Details
Why should I enroll?
In this program, you’ll master deep learning fundamentals that will prepare you to launch or advance a career, and additionally pursue further advanced studies in the field of artificial intelligence. You will study cutting-edge topics such as neural, convolutional, recurrent neural, and generative adversarial networks, as well as sentiment analysis model deployment. You will build projects in Keras and NumPy, in addition to TensorFlow PyTorch. You will learn from experts in the field, and gain exclusive insights from working professionals. For anyone interested in building expertise with this transformational technology, this Nanodegree program is an ideal point-of-entry.
What jobs will this program prepare me for?
This program is designed to build on your skills in deep learning. As such, it doesn't prepare you for a specific job, but expands your skills in the deep learning domain. These skills can be applied to various applications and also qualify you to pursue further studies in the field.
How do I know if this program is right for me?
If you are interested in the fields of artificial intelligence and machine learning, this Nanodegree program is the perfect way to get started!
PROGRAM OVERVIEW - WHY SHOULD I TAKE THIS PROGRAM?
Do I need to apply? What are the admission criteria?
No. This Nanodegree program accepts all applicants regardless of experience and specific background.
What are the prerequisites for enrollment?
Students who are interested in enrolling must have intermediate-level Python programming knowledge, and experience with NumPy and pandas. You will need to be able to communicate fluently and professionally in written and spoken English. Additionally, students must have the necessary math knowledge, including: algebra and some calculus—specifically partial derivatives, and matrix multiplication (linear algebra).
If I do not meet the requirements to enroll, what should I do?
We have a number of Nanodegree programs and free courses that can help you prepare, including:
ENROLLMENT AND ADMISSION
How is this Nanodegree program structured?
The Deep Learning Nanodegree program is comprised of one (1) Term of four (4) months. A Term has fixed start and end dates.
To graduate, students must successfully complete five (5) projects, each of which affords you the opportunity to apply and demonstrate new skills that you learn in the lessons. Each project will be reviewed by the Udacity reviewer network. Feedback will be provided and if you do not pass the project, you will be asked to resubmit the project until it passes.
How long is this Nanodegree program?
Access to this Nanodegree program runs for the period noted in the Term length section above.
See the Terms of Services and FAQs for other policies around the terms of access to our Nanodegree programs.
Can I switch my start date? Can I get a refund?
Please see the Udacity Nanodegree program FAQs found here for policies on enrollment in our programs.
I have graduated from the Deep Learning Nanodegree program but I want to keep learning. Where should I go from here?
Graduates from this Nanodegree program earn guaranteed admitted status into our more advanced Self-Driving Car Engineer or Flying Car Nanodegree programs, subject to payment by student for the cost of enrollment for those Nanodegree programs.
What is “Guaranteed Admission”?
Some Nanodegree programs, due to the complexity of the material, require prerequisites and/or an application process to ensure that students who enroll are qualified to meet the demands of the course. However, in those instances where students have graduated from other Udacity courses that we feel adequately prepare them for our more advanced courses, we will guarantee that they will be allowed to enroll subject to paying the Nanodegree program fees.
TUITION AND TERM OF PROGRAM
What software and versions will I need in this program?
Virtually any 64-bit operating with at least 8GB of RAM will be suitable. Students should also have Python 3 and Jupyter Notebooks installed. For the more intensive portions of the program that come later, we will be providing students with AWS instances where geographically possible.