Study 12 hrs/week and complete in 4 mo.
Classroom opens in 27 days.
This program has been created specifically for students who are interested in machine learning, AI, and/or deep learning, and who have a basic working knowledge of Python programming. Outside of that Python expectation, it's a very beginner-friendly 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, Google Brain, DeepMind, Bengio Lab and more. This is the ideal point-of-entry into the field of AI.
Learn practical skills taught by deep learning experts including Sebastian Thrun, Ian Goodfellow, Andrew Trask, and the Udacity Deep Learning Team.
Work on five specially-designed deep learning projects, and receive detailed feedback on each from our expert reviewers.
Successfully complete the program, and receive guaranteed admission to our Self-Driving Car Engineer, Artificial Intelligence, or Robotics Nanodegree programs!
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.
As a graduate, you earn guaranteed admission into one of three advanced Nanodegree programs. You’ll continue to explore even more deep learning projects alongside groundbreaking new curriculum built with our pioneering industry collaborators. This is how you transform into a job-ready specialist!
Enroll in the Deep Learning Nanodegree Foundation program
Graduate within 4 months
Enroll in one of three advanced Nanodegree programs with guaranteed admission
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.
Mat is a former physicist, research neuroscientist, and data scientist. He did his PhD and Postdoctoral Fellowship at the University of California, Berkeley.
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 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 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 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 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.
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This program requires experience with Python and packages such as Numpy and Pandas. You’ll also need to be familiar with algebra, calculus (multivariable derivatives) and linear algebra (matrix multiplication). See detailed requirements.
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.
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
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
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
Learn to understand and implement the DCGAN model to simulate realistic images, with Ian Goodfellow, the inventor of GANS (generative adversarial networks).Generate Faces
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
Learn to build the deep learning models that are revolutionizing artificial intelligence.
A Nanodegree Foundation program is designed to power your entry into a particular arena of study, with the goal of ensuring that you establish a solid "foundation" in the field. Depending on your longer-term goals, a Nanodegree Foundation program can enhance your existing skillset, help move you forward into advanced studies, or advance your career.
Build and train neural networks from scratch to predict the number of bikeshare users on a given day.
Design and train a convolutional neural network to analyze images of dogs and correctly identify their breeds. Use transfer learning and well-known architectures to improve this model—this is excellent preparation for more advanced applications.
Build a recurrent neural network on TensorFlow to process text. Use it to generate new episodes of your favorite TV show, based on old scripts.
Build a pair of Multi-Layer Neural Networks and make them compete against each other in order to generate realistic faces. Try training them on a set of celebrity faces, and see what new faces the computer comes out with!
Design a deep reinforcement learning agent to control several quadcopter flying tasks, including take-off, hover, and landing.
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