Deep Learning Market is expected to grow at the CAGR of 52.1% till 2025
Demand for AI, DL and ML specialists in the country are expected to see a 60% rise by 2018 due to increasing adoption of automation
Udacity ranked as the most disruptive learning company in the world for 2 years in a row by CNBC
Join a global community of over 50,000 Deep Learning Engineers who have learned with Udacity
What You Learn
Master cutting-edge deep reinforcement learning algorithms with hands-on coding exercises, and challenging, open-ended projects.
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.
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.
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.
Cezanne is an expert in computer vision with a Masters in Electrical Engineering from Stanford University. As a former researcher in genomics and biomedical imaging, she’s applied computer vision and deep learning to medical diagnostic applications.
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.
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.
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.
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.
The program is very didactic, the lessons are detailed and I could learn very much with the projects. These are important adjectives because we are handling with a difficult subject.
The projects are very useful to know about deep reinforcement learning.
Program is generally OK. However, the multi-agent reinforcement learning component has been disappointing. I think the content on that section can be improved significantly.
Excellent first part (intro to DL, value-based methods). Policy based and multi-agent were much lower quality - no supporting materials (e.g., cheatsheet, video notes, quizzes)
The contents from the program were too long to come with long periods without aything to do. It was difficult to keep the momentum
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A small incentive for you to come back and continue your learning with another Nanodegree program
Deep Learning has 1000+ jobs posted on Naukri.com in a month
Deep Learning Engineers earns an average salary in the range of INR 6-20 Lakhs In India
Deep Learning engineers empower technologies like Self Driving Car, Computer Vision and many others
FREQUENTLY ASKED QUESTIONS
We recommend that you complete a course in Deep Learning equivalent to the Deep Learning Nanodegree program prior to entering the program. You will need to be able to communicate fluently and professionally in written and spoken English.
Additionally, you should have the following knowledge:
Basic shell scripting:
Basic statistical knowledge, including:
Intermediate differential calculus and linear algebra, including: