Nanodegree Program
Deep Learning
Enrollment Closing In
Time
1 Four-Month Term
Study 12 hrs/week and complete in 4 mo.
CLASSROOM OPENS
July 10, 2018
Why Take This Program?
In this program, you’ll cover topics like Keras and TensorFlow, convolutional and recurrent networks, deep reinforcement learning, and GANs. You'll learn from authorities such as Sebastian Thrun, Ian Goodfellow, and Andrew Trask, and enjoy access to Experts-in-Residence from OpenAI, Google Brain, DeepMind, Bengio Lab and more. 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, 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 expert reviewers.
Office Hours with Udacity Experts-in-Residence
Enjoy direct access to world-class deep learning practitioners from some of the most innovative organizations in the world. Moderated office hour sessions offer practical, actionable, and insightful guidance and support.
Guaranteed Admission
Successfully complete the program, and receive guaranteed admission to our Self-Driving Car Engineer or Flying Cars and Autonomous Flight Nanodegree programs, subject to your payment of costs of enrollment!
Office Hours with our Experts-in-Residence
Benefit from the opportunity to connect directly with our Udacity Experts-in-Residence, an elite group of data science practitioners working at some of the most data-driven organizations in the world, including Google, Airbnb, IBM Watson, and more. In moderated office hour sessions, you’ll get actionable insights and guidance that will power your progress through our data science courses, and help prepare you for the next steps in your data science future.Connect with our Udacity Experts-in-Residence, working at the most data-driven organizations in the world. You'll get insights and guidance that will power your progress, and prepare you for the next steps in your data science future.
Our Udacity Experts-in-residence
Guaranteed Admission
As a graduate, you earn guaranteed admission, subject to your payment of program enrollment costs, into one of two advanced Nanodegree programs. 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, and pay for, 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 very 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 modern deep learning frameworks (Keras, TensorFlow) to build multi-layer neural networks, and analyze real data.
Your First Neural NetworkConvolutional Neural Networks
Learn how to build convolutional networks and use them to classify images (faces, melanomas, etc.) based on 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 Keras and TensorFlow; perform sentiment analysis and generate new text. Use recurrent networks to generate new text from TV scripts.
Generate TV scriptsGenerative Adversarial Networks
Learn to understand and implement the DCGAN model to simulate realistic images, with Ian Goodfellow, the inventor of GANS (generative adversarial networks).
Generate FacesDeep Reinforcement Learning
Use deep neural networks to design agents that can learn to take actions in a simulated environment. Apply reinforcement learning to complex control tasks like video games and robotics.
Teach a Quadcopter How to Fly
“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)
Andrew Trask
Author of Grokking Deep Learning, Google DeepMind Scholar
Siraj Raval
AI Evangelist, Author, Entrepreneur, and Educator
Learn with the best
Mat Leonard
Program 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
Curriculum Lead
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.
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.
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.
Arpan Chakraborty
Instructor
Arpan is a computer scientist with a PhD from North Carolina State University. He teaches at Georgia Tech (within the Masters in Computer Science program), and is a coauthor of the book Practical Graph Mining with R.
Jay Alammar
Instructor
Jay is a software engineer, the founder of Qaym (an Arabic-language review site), and the Investment Principal at the Riyad Taqnia Fund, a $120 million venture capital fund focused on high-technology startups.
Student Reviews
(1575)
5 stars
75.4%
4 stars
16.6%
3 stars
4.3%
2 stars
1.1%
1 stars
2.6%
Palak S.
Have a nice experience
Jack H.
It's been extremely tough as I've never done anything like this and I kinda just jumped right in. But the topics have been presented in such a way that I'm always interested to come back and see what's next. Also, my mentor has been guiding me through the process of adjusting to this new learning style. Along with my mentor, the forums and the Slack channel have both helped me out tremendously when it comes to smaller questions.
Francisco E.
Gostei muito do curso, consegui aprender sobre várias redes neurais profundas, mas tenho mais interesse por redes recorrentes, já que pretendo usar esta rede no meu doutorado. No geral, o curso é muito bom, traz diversos exemplos, exércícios, os professores são bem didáticos, tem os projetos, experiências de especialistas que trabalham na área. Muito bom. Parabéns.
Rupesh B.
i learned a lot about deep learning and artificial intelligence in general. It has been a great experience and great add-on to my profile.
Mohit B.
Simply the best online course possible on DL.
Nanodegree program
Deep Learning
$599 USD
total
Learn to build the deep learning models that are revolutionizing artificial intelligence.
Success Stories from Our Students
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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 deep reinforcement learning. You will build projects in Keras and NumPy, in addition to TensorFlow. 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 further study in artificial intelligence, machine learning, autonomous vehicles and robotics, 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:
- Introduction to Data Analysis
- Introduction to Computer Science
- Introduction to Python
- Linear Algebra Refresher
- Introduction to Programming Nanodegree program with Data Analysis specialization
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 four (4) 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.How much does the program cost?
The full Nanodegree program consists of one (1) four (4)-month long term at a cost of USD $599 in total program costs.
Payment is due before the term begins.
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.