Deep Learning FAQ

Program Highlights

  • Why should I enroll in the Deep Learning Nanodegree program?

    The Deep Learning Nanodegree program offers you a solid introduction to the world of artificial intelligence. In this program, you’ll master fundamentals that will enable you to go further in the field, launch or advance a career, and join the next generation of deep learning talent that will help define a beneficial new AI-powered future for our world. You will study cutting-edge topics such as Neural Networks, Convolutional Networks, Recurrent Neural Networks, Generative Adversarial Networks, and Deep Reinforcement Learning, and build projects in Keras and NumPy, in addition to TensorFlow. You'll learn from authorities such as Sebastian Thrun, Ian Goodfellow, and Andrew Trask, and participate in our Experts-in-Residence program, where you’ll gain exclusive insights from working professionals in the field. For anyone interested in this transformational technology, this program is an ideal point-of-entry.

  • Who will I be learning from in this program?

    We have a tremendous roster of talent contributing to the curriculum and to your learning experience. This includes Featured Instructors, Experts-in-Residence, Udacity Deep Learning Team members, Project Reviewers, and more.

  • Who are your featured instructors?

    In this program, you'll learn from Featured Instructors such as Sebastian Thrun (Founder, Google X, Udacity), Ian Goodfellow (Inventor of GANs, Author of Deep Learning, from MIT Press), and Andrew Trask (Author of Grokking Deep Learning, Google DeepMind Scholar).

  • What do you mean by Experts-in-Residence?

    Our Udacity Experts-in-Residence are an elite group of deep learning practitioners working at some of the most innovative organizations in the world, including OpenAI, GoogleBrain, DeepMind, Bengio Lab and more. During moderated office hour sessions, you will benefit from direct access to these experts, who will answer questions, provide support and guidance, and deliver the kind of actionable insights only working professionals can provide.

  • How do Project Reviews work?

    Every time you submit a project, you will receive in-depth, personalized feedback on your project submission from one of our project reviewers. They will tell you what you got right and wrong, provide guidance on what you should try next, and give suggestions on how you might go even further with your project.

  • How do I know if this program is right for me?

    This program offers an ideal introduction to the world of deep learning—a transformational technology that is reshaping our future, and driving amazing new innovations in Artificial Intelligence. If you're interested in fields such as Artificial Intelligence, Machine Learning, Autonomous Transportation, and Robotics, this is the perfect way to get started! Please see below for enrollment prerequisites.

  • 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. Additionally, students must have the necessary math knowledge, including: algebra and some calculus—specifically partial derivatives, and matrix multiplication (linear algebra).

  • What courses do you recommend if I do not meet these prerequisites?

    For students who need to refresh skills for this program, we suggest the following courses from Udacity:

  • Is this a career-ready program?

    We do not consider this program to be a career-ready program; rather, this should be understood as both an excellent introduction to the field, and—via the guaranteed admission component—an ideal stepping stone to our career-focused offerings. Career resources are not a part of this program, and students should not expect to emerge from the program job-ready in deep learning.

  • What frameworks/technologies will we be using in the course?

    In the first few lessons of this course we will be using the scikit learn and numpy Python libraries. You will then progress into Keras and TensorFlow, using them to complete your projects.

  • What type of computer build should I have?

    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—you will get an email with a credit code you can use with your AWS account. If you already have an AWS account, please use the same account when applying the credit code.

  • Will I receive a credential when I graduate, as with other Nanodegree programs?

    Yes! You will receive a Deep Learning Nanodegree program credential when you successfully complete the program requirements on time.

  • Will I have access to the material even after the term ends?

    No. You will retain access to the program materials for a period of time after graduation and you may download certain materials for your own records if you wish. Please note however, that students who leave the program—or who are removed from the program for failure to meet deadlines—prior to successfully graduating, will cease to have access.

  • I know I'll be excited to continue my studies after I graduate, where do I go from here?

    That's the best part! Graduates from this program earn guaranteed admission into our Self-Driving Car Engineer, Artificial Intelligence, or Robotics Nanodegree programs.

  • I understand that as a Deep Learning Nanodegree program graduate, I earn guaranteed admission into either the Self-Driving Car Engineer, Artificial Intelligence, or Robotics Nanodegree programs. I'd like to enroll now. Can I?

    No, you cannot take advantage of the guaranteed admission offer until after you successfully graduate from the Deep Learning Nanodegree program. All details around the process of transitioning from your Deep Learning program to one of our advanced programs will be communicated to you via email upon successful completion.

    Please do not take action with regards to a new enrollment until you receive the necessary details.

Program Structure

  • How is the program structured?

    The Deep Learning Nanodegree program consists of one term that is four months long. Students must complete the required projects before the end of the term to earn their credential and graduate. The term costs $599, and payment must be made at the beginning of the term.

  • Is this program self-paced?

    No. The Deep Learning Nanodegree program is a four-month-long program with fixed start and end dates, with a short additional period of time if needed. In this program, we have project deadlines meant to help you keep pace with your peers, and to graduate within four months. See specific details in the deadlines policy section.

  • What kind of weekly time commitment should I expect?

    You should plan to spend approximately 8-12 hours per week throughout the entire term, in order to complete this program on time. This is an average, so some students may require more or less than the allotted time frame.

  • How long will I have access to the content?

    The program is paced to be completed in four months. All students will have access to the content for an additional four weeks to submit and pass all the projects.


  • What happens if I don't complete a project on time?

    It is strongly recommended that you complete each project on time to ensure you meet graduation requirements. To graduate, you must complete, submit, and meet expectations for all required projects within four months of your start date. While there is no penalty for missing a project deadline, missing one puts you at risk to be removed from the program if you do not stay on track and complete all required projects before the term ends. Finally, by keeping pace with your fellow students, you'll gain much more value from forums and Slack channels!

  • What are my options if I don't complete the program within four months?

    If you do not complete the term by the term deadline, you will receive one free four-week extension, which will be automatically applied to your account. If you do not complete the term within the extension period, you will be removed from the program and will no longer be able to access course content. To resume access to the course, you would need to re-enroll in a new term and pay the associated enrollment fees again. Your progress will carry over to the new term, so you will be able to continue. You are also welcome to apply to either the Artificial Intelligence, Robotics, or Self-Driving Car Engineer Nanodegree programs, but your admission is not guaranteed. This is a benefit reserved only for graduates of this program.


  • Can I enroll in the program at any time?

    Yes! We enroll students on a rolling basis, and you will automatically be added to the next available term once you've successfully enrolled.

  • Once I am enrolled, when does the classroom open, and when do I start?

    Once enrollment closes, we will notify you of the date when the classroom will open.

  • What happens if I don't enroll by the enrollment closing date?

    No problem! As soon as the next enrollment period opens, you can enroll in the next term.

  • Is the tuition cost for instructional costs all-inclusive?

    Yes! It is a one-time payment, and should cover the program features and benefits.

  • Are there scholarships available for this program?

    All current scholarship opportunities are posted on our scholarships page.

  • Is there a free trial period for this program?

    No, there is no free trial period for this program.

  • What is the refund policy?

    There is a seven day refund policy. Send our support team an email within seven days of the date your term begins, to request that your enrollment be canceled and payment refunded.

Guarenteed Admissions to the Artificial Intelligence, Self-Driving Car, or Robotics Nanodegree programs

  • When do I apply to the Artificial Intelligence, Robotics or Self-Driving Car Engineer Nanodegree programs?

    You are eligible to apply to one of these programs after graduating from your Deep Learning Nanodegree program. Upon graduating from the program, you will be contacted via email with further instructions along with the upcoming enrollment periods and start dates.

  • Should I apply early to guarantee a spot?

    No, you should wait until you receive your post-graduation enrollment instructions via email.

  • What if I've already applied?

    If you are admitted after you have applied, you can simply choose not to enroll in the program since you will already be guaranteed admission upon successful graduation from the Deep Learning Nanodegree program.

  • Can I add this program and its projects to my Udacity Professional Profile?

    Yes! Even though the Deep Learning Nanodegree program is not a career-ready program, you can still add it to your profile. To do so, you will need to manually create a custom card under the Education category of your profile.

    As to projects, they are automatically added into your Profile from career-ready Nanodegree program, but you may manually add projects from this program by creating a custom Project card.

  • Can I take this program while being enrolled in another Nanodegree program, and what are the benefits of doing so?

    Our programs require a serious time commitment from students, so while we do not recommend doing so, we do not prohibit concurrent enrollments. This is an intensive, paced program, and students must proceed throughout the programs at the required rate of progress. To make the most of your experience, we believe you are best served by focusing on one program at a time and being fully immersed in the unique structure and pacing. You can always take one after the other!

  • Is this program available internationally?

    All eligible students, according to our Terms of Service, are welcome to enroll in the English-language version of this program. Some countries may have localized pricing and other language options. Certain countries may have issues with access to AWS instances.

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