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Udacity’s Deep Reinforcement Learning Nanodegree Program – Ratings and Review!

Udacity's Deep Reinforcement Learning Nanodegree Program - Ratings and Review!
Data Science

Udacity’s Deep Reinforcement Learning Nanodegree Program – Ratings and Review!

78 Reviews


In Udacity’s Deep Reinforcement Learning Nanodegree Program, learn the deep reinforcement learning skills that are powering amazing advances in AI.

Udacity's Deep Reinforcement Learning Nanodegree Program - Ratings and Review!

Enroll in Udacity’s Deep Reinforcement Learning Nanodegree Program today!

Overview of the Udacity’s Deep Reinforcement Learning Nanodegree Program

In Udacity’s Deep Reinforcement Learning Nanodegree Program, learn cutting-edge deep reinforcement learning algorithms—from Deep Q-Networks (DQN) to Deep Deterministic Policy Gradients (DDPG). Apply these concepts to train agents to walk, drive, or perform other complex tasks, and build a robust portfolio of deep reinforcement learning projects

Udacity’s Deep Reinforcement Learning Nanodegree Program Syllabus

1. Foundations of Reinforcement Learning

Master the fundamentals of reinforcement learning by writing your own implementations of many classical solution methods.

2. Value-Based Methods

Apply deep learning architectures to reinforcement learning tasks. Train your own agent that navigates a virtual world from sensory data.

3. Policy-Based Methods

Learn the theory behind evolutionary algorithms and policy-gradient methods. Design your own algorithm to train a simulated robotic arm to reach target locations.

4. Multi-Agent Reinforcement Learning

Learn how to apply reinforcement learning methods to applications that involve multiple, interacting agents. These techniques are used in a variety of applications, such as the coordination of autonomous vehicles.

Instructors of Udacity’s Deep Reinforcement Learning Nanodegree Program

Here is a list of instructors associated with the Udacity Nanodegree Program:

1. Alexis Cook (Curriculum Lead)

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.

2. 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.

3. 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.

4. 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.

5. Cezanne Camacho (Curriculum Lead)

Cezanne is a machine learning educator with a Masters in Electrical Engineering from Stanford University. As a former researcher in genomics and biomedical imaging, she’s applied machine learning to medical diagnostic applications.

6. Dana Sheahan (Content Developer)

Dana is an electrical engineer with a Masters in Computer Science from Georgia Tech. Her work experience includes software development for embedded systems in the Automotive Group at Motorola, where she was awarded a patent for an onboard operating system.

7. Chhavi Yadav (Content Developer)

Chhavi is a Computer Science graduate student at New York University, where she researches machine learning algorithms. She is also an electronics engineer and has worked on wireless systems.

8. Juan Delgado (Content Developer)

Juan is a computational physicist with a Masters in Astronomy. He is finishing his PhD in Biophysics. He previously worked at NASA developing space instruments and writing software to analyze large amounts of scientific data using machine learning techniques.

9. Miguel Morales (Content Developer)

Miguel is a software engineer at Lockheed Martin. He earned a Masters in Computer Science at Georgia Tech and is an Instructional Associate for the Reinforcement Learning and Decision Making course. He’s the author of Grokking Deep Reinforcement Learning.

Review: “Definitely worth enrolling in!”

After successfully completing the Nanodegree program, you will find that the program is very comprehensive and teaches you everything promised by the program.

Plus, the certification is also really helpful since this Udacity Nanodegree Program is well-recognized amongst multiple industries.

Enroll in Udacity’s Deep Reinforcement Learning Nanodegree Program today!

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