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Learn 12 hours/week, to graduate in 4 months
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.
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.
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.
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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).
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
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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Enter the field of Artificial Intelligence (AI) through our Deep Learning Nanodegree program, and start building your own deep neural networks.
Admissions Closes: 11 July
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All the above payment plans include the costs of Unlimited project reviews (by Udacity's global reviewer network), in-classroom mentorship support through chat with industry experts and Access to global community of Udacity students pursuing the same Nanodegree.
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