Build your Deep Learning foundations, and earn your Udacity credential!

Artificial Intelligence is transforming our world in dramatic and beneficial ways, and Deep Learning is powering the progress. Together with Siraj Raval, Udacity provides a dynamic introduction to this amazing field, using weekly videos, exclusive projects, and expert feedback and review to teach you the foundations of this future-shaping technology.

Meet Siraj

Hello World, it's Siraj! I'm a Data Scientist, bestselling author, and YouTube star. I make videos that teach people how to use machine learning to create game bots, chatbots, self-driving cars, programs that create art and music, stock prediction models, and much more. I'm proud to be an exclusive Udacity partner, and excited to be your host for this amazing program.

Features 1
Amazing content & live sessions

Amazing Content, Exclusive Access

Concept mastery with Udacity experts, code walkthroughs with Siraj Raval, exclusive lessons with Andrew Trask and Ian Goodfellow, and more!

Projects with expert feedback

Unique Projects, Expert Feedback

Work on five specially-designed Deep Learning projects, and benefit from expert feedback on each.

Features 2
Guaranteed Admission

Guaranteed Admission

Successfully complete the program, and receive guaranteed admission to our Self-Driving Car, Artificial Intelligence, or Robotics Nanodegree programs!

Earn a Udacity Foundation Nanodegree

Earn your Udacity Nanodegree Foundation credential

Every program graduate receives a Udacity credential affirming their mastery of Deep Learning foundations.

Program Syllabus

Prerequisite Knowledge

Programming knowledge needed: Basic to intermediate Python, experience with Numpy. Anaconda and Jupyter Notebooks.


Math knowledge needed: Algebra. Partial derivatives. Matrix multiplication.

Need to Prepare?

Students lacking the requisite Python knowledge can take the Introduction to Python to address this requirement.

Prepare now with Introduction to Python.

Deep Learning is a transformational technology that we see around us every day in medical imaging, Google searches, self-driving cars, and more. We are just at the start of what this technology can do for us, and I can't wait to see what our students build next.

Sebastian Thrun

Founder, Udacity

  • Part 1

    Introduction

    Get introduced to the program and explore various ways deep learning networks are applied. Also, you’ll get up to speed on the tools and math you’ll be using in the program with some introductory lessons.

  • Part 2

    Neural Networks

    Learn the basics of neural networks and build your first neural network with Python and Numpy. You’ll also get an introduction to TensorFlow and how to use it to build deep neural networks.

  • Part 3

    Convolutional Neural Networks

    A few years ago, convolutional networks changed the computer vision field by enabling powerful feature detection in images. In this lesson, you’ll learn how to build convolutional networks and use them to classify images based on the objects that appear in them.

  • Part 4

    Recurrent Neural Networks

    Recurrent neural networks are able to learn information about sequences in data, such has the order of words in text. Recurrent networks also work great as feature extractors for text which you can use for things like sentiment analysis. You’ll use recurrent networks to generate new text and translate from one language to another.

  • Part 5

    Generative Adversarial Networks

    Generative adversarial networks (GANS) pit two neural networks in competition, allowing these networks to model reality with amazing accuracy. Ian Goodfellow, the inventor of GANs, will show you how these fascinating models work and how to build them.

Projects You Will Build

Project 1 - Your first neural network
Project 1

Your first neural network

Build and train your own Neural Network from scratch to predict the number of bikeshare users on a given day.

Build and train your own Neural Network from scratch to predict the number of bikeshare users on a given day.

Project 2 - Image Classification
Project 2

Image Classification

Classify images from the CIFAR-10 dataset using a convolutional neural network.

Classify images from the CIFAR-10 dataset using a convolutional neural network.

Project 3 - Generate TV scripts
Project 3

Generate TV scripts

Use deep learning to generate new scripts for your favorite TV show.

Use deep learning to generate new scripts for your favorite TV show.

Project 4 - Translate a Language
Project 4

Translate a Language

Translate from one language to another

Translate from one language to another

Project 5 - Generate Faces
Project 5

Generate Faces

Compete two neural networks against each other to generate realistic faces.

Compete two neural networks against each other to generate realistic faces.

Start Building your
Deep Learning Foundations Today

Enroll for $599 $800

4 days left to enroll

Enrollment ends May 30, 2017 at 11:59 PM. Classroom opens on May 31, 2017.

Siraj's Deep Learning FAQ

Program Highlights

  • What is a “Nanodegree Foundation program,” and how does it differ from your existing Nanodegree programs?

    A Nanodegree Foundation program is designed to facilitate your entry into a particular arena of study, with the goal of ensuring that you establish your “foundations” in the field. Depending on your longer-term goals, a Foundation program can enable you to enhance an existing skillset, move forward into deeper and/or more advanced academic studies, or prepare for a career move that requires a fuller understanding of certain technologies and concepts.

  • Who are the instructors and subject-matter experts for this program?

    Udacity expert Mat Leonard is the primary instructor. He will cover all the hands–on material to ensure students are able to successfully complete their projects, and develop the knowledge and skills needed to continue on in our advanced Nanodegree programs.

    Siraj Raval is our primary consulting contributor, and he provides short lectures focusing on trends and high–level topics in the Deep Learning space—along with detailed code walkthrough sessions.

    Additionally, we have several world–class Deep Learning practitioners and luminaries that make appearances in the classroom, including Vincent Vanhouke, Ian Goodfellow, Sai Soundararaj, Andrew Trask, Luis Serrano, and more. They cover key topics such as TensorFlow, Generative Adversarial Networks, Sentiment Analysis, Model Evaluation Validation, and more.

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

    This program offers an excellent introduction to the world of Deep Learning—a transformational technology that is going to reshape our future, and drive amazing new innovations in AI. If you've been interested in the machine learning space, but haven't felt comfortable or qualified to dive in, this is the perfect way to get started! Please see below for prerequisites for enrollment.

  • What are the prerequisites for enrollment?

    This program has been created specifically for students who are interested in machine learning, AI, and/or deep learning. Students who are interested in enrolling must have intermediate Python programming knowledge, experience with Numpy, experience using Anaconda and Jupyter Notebooks. Additionally, students must have the necessary math knowledge including: algebra and some calculus - specifically partial derivatives, and matrix multiplication (linear algebra) to be successful.

  • Is this a career-focused program?

    Given the focus on establishing knowledge foundations, we do not consider this a career-focused 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 Foundation program, and students should not expect to emerge from the program job-ready. It is of course possible that your previous experience, combined with what you learn here, will enable you to pursue certain roles after you graduate, but job preparation is not a program component.

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

    In the first few lessons of this course we'll be using the scikit learn and numpy Python libraries. We'll then progress into learning Tensorflow, and using it to complete our 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—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 Foundation credential when you successfully complete the program on time.

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

    That's the best part! Graduates from this program are guaranteed admission into our Self-Driving Car or Artificial Intelligence programs, and even our recently-launched Robotics Nanodegree program! You even receive a $100 credit applied to those enrollments!

  • I understand that as a Deep Learning Foundations student, I get guaranteed admission into the Self-Driving Car, Artificial Intelligence, or Robotics Nanodegree program. 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 Foundation 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. Please do not take action with regards to a new enrollment until you receive the necessary details.

Structure and Deadlines

  • How is the program structured? Are there hard deadlines throughout, or is the program self-paced?

    The program is project-based, and each project has a deadline. It is strongly recommended that you complete each project on time, in order to ensure you meet graduation requirements. To graduate, you must complete, submit, and meet expectations for all required projects within 6 months of your start date. Failure to meet the 6-month deadline means you wouldn’t graduate nor earn your credential. Additionally, you are no longer eligible to receive automatic admission into the Artificial Intelligence, Robotics, or the Self-Driving Car Nanodegree programs.

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

    It is critical to your success that you maintain good standing with regards to project deadlines. If you’re keeping pace with your fellow students, forums and Slack channels have much more value. Otherwise, your peer network can break down, because you're no longer working on the same content as the rest of your class. Falling behind also jeopardizes your ability to meet the 6-month requirement for graduating and earning your credential, and receiving your guaranteed admission into the Artificial Intelligence, the Self-Driving Car or the Robotics Nanodegree programs. That said, please know there is no penalty for missing a project deadline. We recognize that students learn at different paces, so remember that within the boundaries of your start and end dates, you enjoy some flexibility as far as pacing goes.

  • What are my options if I don't complete the program within 6 months?

    You are welcome to re-enroll in the same program, but your progress is not saved, and it does not carry over—you will start at the beginning again. Upon re-enrollment, students will be responsible for paying for the necessary costs. You are also welcome to apply to either the Artificial Intelligence, Robotics, or the Self-Driving Car Nanodegree programs, but your admission is not guaranteed.

  • What kind of weekly time commitment should I expect?

    On average, we find students spending 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 than the allotted time frame, or less.

About Siraj Raval

  • What is Siraj Raval's role in the program?

    Siraj is our exclusive partner for this program, a co–host of the program, and an expert contributor to the program curriculum. Siraj’s role is to provide students introductory videos to the different topics we cover.

  • How is this program different from the videos on Siraj's YouTube channel?

    As Siraj’s YouTube channel engages students on various topics, the content that we provide at Udacity dives deeper into the concepts and lessons that are introduced by Siraj. This program offers weekly problem sets, and five unique projects that deepen your understanding of the content in the program. Each project will receive expert feedback and review. You will also have the full support of our dedicated team to answer your questions, support your goal-setting and completion, and ensure that you successful graduate and earn your credential. This is all in addition to exclusive content from Siraj and Udacity instructors that is only available to enrolled students!

Enrollment

  • Can I enroll in the program at any time?

    No, you can only enroll during an open enrollment period which generally last 5-10 days. If enrollments are closed, you can join the waiting list to be notified when our next open enrollment period will begin.

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

    Once enrollment closes, we will open the classroom. Your start date will be available in the classroom.

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

    No problem! As soon as the next enrollment period opens, you can enroll for the next term. Students who don’t enroll by enrollment date will not be admitted into the current start date. In order to keep all students on track for graduation, we must adhere to a strict closing date.

  • Is the tuition cost all-inclusive?

    Yes! It is a one-time payment, and covers all program features and benefits.

Guaranteed 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 Nanodegree programs?

    You are eligible to apply to one of these programs after graduating from your Deep Learning Nanodegree Foundation program—the guaranteed admission benefit is only for graduates. Graduates will be notified via email on further instructions.

  • Should I apply to the Artificial Intelligence, Robotics, or Self-Driving Car Nanodegree programs to guarantee a spot?

    We do not recommend our students apply to Artificial Intelligence, Robotics, or Self-Driving Car Nanodegree program until they have received more information upon successful graduation from Deep Learning Nanodegree Foundation program. Since Deep Learning graduates will be guaranteed admission on a later date, plus the $100 credit is only granted to Deep Learning graduates, currently enrolled students are encouraged to wait for more information via email.

  • What if I've already applied to the Artificial Intelligence, Robotics, or Self-Driving Car Nanodegree programs?

    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 Deep Learning Nanodegree Foundation program.

  • How do I apply the $100 credit towards the Artificial Intelligence, Robotics, or Self-Driving Car Nanodegree programs?

    After you graduate from the Deep Learning Nanodegree Foundation program, you will receive a one-time use coupon that is valid for a $100 credit towards the first term of the Artificial Intelligence, Robotics, or Self-Driving Car Nanodegree programs. This coupon will expire December 31, 2017. You will use this coupon when you make your first payment for whichever program you've selected. This credit is not applicable or transferable if students are dual enrolled in Deep Learning and any of the advanced Nanodegrees mentioned above. This is an exclusive offer to students who graduate from Deep Learning and are guaranteed admission into one of the above Nanodegrees.

Other

  • Has the pricing for this program been the same since the start?

    The current pricing is the standard pricing for this program. We did offer a special discounted rate when the program first launched, but that was specific to the debut of the program. There are no plans to repeat that initial offer, and no other changes expected.

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

    Deep Learning is already becoming a fundamental part of the software stack, and it's very soon going to be a part of every programmer's toolkit regardless of platform or industry. We want you to be able to apply Deep Learning techniques to your field, so a simultaneous enrollment is definitely recommended if you want to stand out for being truly on the cutting edge of modern machine-learning techniques!

  • 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.

  • Are there scholarships available for this program?

    At this time we are not awarding scholarships for this program.

  • What is the refund policy?

    There is a 7-day refund policy. Send us an email within 7 days of the date your term begins, to request that your enrollment be canceled and payment refunded.

  • Are deferments an option if I'm enrolled, but not ready to start yet?

    No, deferments are not an option. We ask that you please make sure to only enroll for a term if you are able to commit to the entire time frame. If you are already enrolled, and opt to not complete the term, we cannot guarantee future admission.

Thanks for your interest!

We'll be in touch soon.

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