Nanodegree Program

Deep Learning

Build Deep Learning Models Today

Deep learning is driving advances in artificial intelligence that are changing our world. Enroll now to build and apply your own deep neural networks to challenges like image classification and generation, time-series prediction, and model deployment.

  • Estimated Time
    4 months

    At 12 hrs/week

  • Enroll by
    December 24, 2019

    Get access to classroom immediately on enrollment

  • Prerequisites
    Basic Python

    See prerequisites in detail

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What You Will Learn

Download Syllabus
Syllabus

Deep Learning

Become an expert in neural networks, and learn to implement them using the deep learning framework PyTorch. Build convolutional networks for image recognition, recurrent networks for sequence generation, generative adversarial networks for image generation, and learn how to deploy models accessible from a website.

Master building and implementing neural networks for image recognition, sequence generation, image generation, and more.

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4 months to complete

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 Patterns
  • Convolutional 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 Classifier
  • Recurrent 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 scripts
  • Generative 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 Faces
  • Deploying 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
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The share of jobs requiring AI skills has grown 4.5x since 2013

All Our Nanodegree Programs Include

Real-world projects from industry experts

With real world projects and immersive content built in partnership with top tier companies, you’ll master the tech skills companies want.

1-on-1 technical mentor

Get a knowledgeable mentor who guides your learning and is focused on answering your questions, motivating you and keeping you on track.

Personal career coach and career services

You’ll have access to career coaching sessions, interview prep advice, and resume and online professional profile reviews to help you grow in your career.

Flexible learning program

Get a custom learning plan tailored to fit your busy life. Learn at your own pace and reach your personal goals on the schedule that works best for you.
Succeed with Personalized Services
We provide services customized for your needs at every step of your learning journey to ensure your success!
Experienced Project Reviewers
Individual 1-on-1 Mentorship
Personal Career Coach
Get personalized feedback on your projects
Reviews By the numbers
2000+ project reviewers
1.8M projects reviewed
4.85/5 reviewer ratings
3 hour avg project review turnaround time
Reviewer Services
  • Personalized feedback
  • Unlimited submissions and feedback loops
  • Practical tips and industry best practices
  • Additional suggested resources to improve
Succeed with Personalized Services
We provide services customized for your needs at every step of your learning journey to ensure your success!
Project Reviewers
1-on-1 Mentors
Career Coaching
Get personalized feedback on your projects
Reviews By the numbers
2000+ project reviewers
1.8M projects reviewed
4.85/5 reviewer ratings
3 hour avg project review turnaround time
Reviewer Services
  • Personalized feedback
  • Unlimited submissions and feedback loops
  • Practical tips and industry best practices
  • Additional suggested resources to improve

Learn with the best

Mat Leonard
Mat Leonard

Instructor

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
Luis Serrano

Instructor

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
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
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
Jennifer Staab

Instructor

Jennifer has a PhD in Computer Science and a Masters in Biostatistics; she was a professor at Florida Polytechnic University. She previously worked at RTI International and United Therapeutics as a statistician and computer scientist.

Sean Carrell
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
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
Jay Alammar

Instructor

Jay has a degree in computer science, loves visualizing machine learning concepts, and is the Investment Principal at STV, a $500 million venture capital fund focused on high-technology startups.

Student Reviews

4.6

(1903)

Sheroy M.

This program was by far the best course I've found on Deep Learning and AI. The course content has been designed by industry experts in this field of work. Udacity follows a unique style of teaching, by introducing you to a topic, giving you a hands-on assignment to apply your learning, and finally following up with a solution. This style of learning, along with the quality of videos, variety of experienced instructors, and regular hands-on projects makes this course really effective to learn the latest techniques in AI. I would highly recommend this course.

Alessandro G.

very nice

Uday P.

The program is great and very good support from mentor. Happy so far and meets my expectations.

Kostyantyn B.

Overall, this is a very strong program. The instructors manage to cover a lot of material while still providing enough depth. The projects are quite challenging and very much hands-on. Considering the breadth of the subject of the Deep Learning and the time constraints (3 months), I think this is as good as it gets. So yes, I do recommend it.

Songting D.

Anita G.

The course is very interesting, state-of-the-art and absolutely matched my expectations. I enjoy it very much. At this moment I have a little delay since I take another nanodegree in parallel but I think I can get over it soon.

The Udacity Impact

Numbers don't lie. See what difference it makes in career searches.*

84%
Better Jobs

Career-seeking and job-ready graduates found a new, better job within six months of graduation.

$24,000
Salary Increase

Average salary increase for graduates who found a new, better job within six months of graduation.

Program Details

    PROGRAM OVERVIEW - WHY SHOULD I TAKE THIS PROGRAM?
  • 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!

    ENROLLMENT AND ADMISSION
  • 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?
    TUITION AND TERM OF PROGRAM
  • How is this Nanodegree program structured?

    The Deep Learning Nanodegree program is comprised of content and curriculum to support five (5) projects. We estimate that students can complete the program in four (4) months working 10 hours per week.

    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 length of time specified in the payment card above. If you do not graduate within that time period, you will continue learning with month to month payments. See the Terms of Use and FAQs for other policies regarding 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 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.

    SOFTWARE AND HARDWARE - WHAT DO I NEED FOR THIS 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.

Deep Learning

Build Deep Learning Models Today