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.

Enroll by June 12

Classes start in

  • Time
    4 Months

    Learn 12 hours/week, to graduate in 4 months

  • Classroom opens
    June 12, 2018
  • Prerequisites
    Python, Machine Learning, Maths

    See prerequisites in detail

  • Price
    2250 SAR

    One-time payment

Why Take This Program?

In this program, you’ll cover topics like Keras and TensorFlow, convolutional and recurrent networks, deep reinforcement learning, and GANs. You'll learn from authorities such as Sebastian Thrun, Ian Goodfellow, and Andrew Trask, and enjoy access to Experts-in-Residence from OpenAI, GoogleBrain, DeepMind, and more. This is the ideal point-of-entry into the field of AI.


Why Take This Program?

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

Deep Learning Foundation
SAR 2250

total

Enter the field of Artificial Intelligence (AI) through our Deep Learning Nanodegree Foundation program, and start building your own deep neural networks.

Enroll Now
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 expert reviewers.

Office Hours with Udacity Experts-in-Residence
Office Hours with Udacity Experts-in-Residence

Office Hours with Udacity Experts-in-Residence

Enjoy direct access to world-class deep learning practitioners from some of the most innovative organizations in the world. Moderated office hour sessions offer practical, actionable, and insightful guidance and support.

Guaranteed Admission

Guaranteed Admission

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

Office Hours with our Experts-in-Residence

Benefit from the opportunity to connect directly with our Udacity Experts-in-Residence, 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. In moderated office hour sessions, you’ll get actionable insights and guidance that will power your progress through the program, and help prepare you for the next steps in your deep learning future.

What You Will Learn

Syllabus

Deep Learning

Become an expert in neural networks, and learn to implement them in Keras and TensorFlow. Build convolutional networks for image recognition, recurrent networks for sequence generation, generative adversarial networks for image generation, and more.

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

See fewer details

4 Months to complete

Prerequisite Knowledge

You’ll need intermediate experience with Python (incl. packages such as Numpy and Pandas) and basic knowledge of machine learning to start this program. You’ll also need to be familiar with calculus (multivariable derivatives) and linear algebra (matrix multiplication).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.

    Your First Neural Network
  • 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.

    Teach a Quadcopter How to Fly

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
Siraj Raval
Siraj Raval
AI Evangelist, Author, Entrepreneur, and Educator

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

Curriculum Lead

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.

Arpan Chakraborty
Arpan Chakraborty

Instructor

Arpan is a computer scientist with a PhD from North Carolina State University. He teaches at Georgia Tech (within the Masters in Computer Science program), and is a coauthor of the book Practical Graph Mining with R.

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.

  • 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.
  • 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.
  • Is the tuition cost all-inclusive?
    Yes! It is a one-time payment, and covers all program features and benefits.
  • 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.

Deep Learning