Nanodegree Program

Deep Learning

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 produce amazing solutions to important challenges.

ENROLLMENT CLOSING IN

Why Take This Nanodegree Program?

In this program, you’ll cover Convolutional and Recurrent Neural Networks, Generative Adversarial Networks, Deployment, and more. You’ll use PyTorch, and have access to GPUs to train models faster. You'll learn from authorities like Sebastian Thrun, Ian Goodfellow, Jun-Yan Zhu, and Andrew Trask. This is the ideal point-of-entry into the field of AI.


Why Take This Nanodegree Program?

AI-driven global software revenue will top $30B in 2020

Expert Instructors
Expert Instructors

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

Unique Projects, Personalized Feedback

Work on five specially-designed deep learning projects, and receive detailed feedback on each from our mentors.

Deploy Your Own Sentiment Analysis Model
Deploy Your Own Sentiment Analysis Model

Deploy Your Own Sentiment Analysis Model

You’ll get hands-on experience deploying and monitoring a model using PyTorch and Amazon SageMaker. By teaching these essential skills, we are preparing our students to be indispensable members of AI product teams.

Guaranteed Admission

Guaranteed Admission

Successfully complete the program, and receive guaranteed admission to our Self-Driving Car Engineer, Artificial Intelligence, or Flying Cars and Autonomous Flight Nanodegree programs, subject to your payment of costs of enrollment!

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.

Step 1

Enroll in the Deep Learning Nanodegree program

Step 2

Graduate within 4 months

Step 3

Enroll in, and pay for, one of two advanced Nanodegree programs with guaranteed admission

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.

See fewer details

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 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.

    Predicting Bike-Sharing Patterns
  • Convolutional 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 Classifier
  • Recurrent 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 scripts
  • Generative 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 Faces
  • Deep 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.

    Deploying a Sentiment Analysis Model
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
Sebastian Thrun
Founder, Google X, Self-Driving Car Pioneer
Ian Goodfellow
Ian Goodfellow
Inventor of GANs, Author of Deep Learning (MIT Press)
Andrew Trask
Andrew Trask
Author of Grokking Deep Learning, Google DeepMind Scholar
Jun-Yan Zhu
Jun-Yan Zhu
RESEARCHER AT MIT CSAIL AND COAUTHOR OF CYCLEGAN

Learn with the best

Mat Leonard
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
Luis Serrano

HEAD OF CONTENT

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
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
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.

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.

Jay Alammar
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.

Jennifer Staab
Jennifer Staab

Instructor

Jennifer has a PhD in Computer Science, Masters in Biostatistics, and 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.

Nanodegree program
Deep Learning
$999 USD

total

Learn to build the deep learning models that are revolutionizing artificial intelligence.

Deep Learning