Estimated Time
4 months
At 12 hrs/week
Enroll by
May 28, 2019
Get access to classroom immediately on enrollment
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What You Will Learn
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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
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. Along with easy monthly payments you can learn at your own pace and reach your personal goals.
Succeed with Personalised Services
We provide services customised 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
Experienced Project Reviewers
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 Personalised Services
We provide services customised for your needs at every step of your learning journey to ensure your success!
Project Reviewers
1-on-1 Mentors
Career Coaching
Experienced Project Reviewers
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
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
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
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 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
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 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
Top Reviews
(1764)
5 stars
76.0%
4 stars
16.2%
3 stars
4.3%
2 stars
1.1%
1 stars
2.6%
James T.
Great! Although I wished the final project was reenforcement learning
Patricia R.
yes, it is one of the best practical course ever fr deep learning
Eigenvalue l.
Very useful content and interesting projects
Andrew B.
Excellent content and projects!
Farhan Z.
yes it is quite comprehensive in every way. Especially liked the extra material on Attention based models and a comprehensive view of GANs. The material on LSTM internal structure was excellent and the best description of LSTM gating structure I could find anywhere. Great Job!!!
The Udacity Impact
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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
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 content and curriculum to support five (5) projects. Once you subscribe to a Nanodegree program, you will have access to the content and services for the length of time specified by your subscription. 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 your subscription plan. See the Terms of Use and FAQ for other policies around the terms of access to our Nanodegree programs.
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.
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.