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An Introduction to AI Unit 5: LSTMs, Reinforcement Learning and Generative Adversarial Networks

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1 Hour
Unit overview

This unit will further explore some more advanced types of neural networks. We will look at models that can handle sequences better and time domain models. We will first explore long short-term memory models (LSTMs), which are used for more difficult sequence problems, such as language processing. We will then look at reinforcement learning where systems obtain, and learn from, data from the environment and the use of the AI system itself, e.g. having a game play itself. Finally, we will discuss generative adversarial networks. which can be used to make artificial intelligence creative. This unit will include some further application examples.

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Level Technical
Partner Details

Dr. Ronjon Nag

Dr. Ronjon Nag has an Engineering PhD (Cambridge), an SM in Management Science (MIT) and a B.Sc. in Electrical Engineering (Birmingham). He is president of the R42 Institute and he became a Stanford University Interdisciplinary Distinguished Careers Institute Fellow at the Center for Study for Language and Information in 2016. He works on the Boundaries of Humanity Project looking at intelligence in humans, animals and machines in the age of biotechnology and artificial intelligence. He teaches at Stanford Medical School. He is an active Advisor and Board Member to some 70 AI and Biotech companies. He has also been awarded the IET Mountbatten Medal for contributions to the mobile phone industry.

Type Unit
What you will learn
By the end of this unit, you will:

• Understand long short-term memory models.
• Understand reinforcement learning.
• Understand generative adversarial networks.

Who should learn
This introductory course is open to learners of all levels. No computer science or programming experience is needed, but an understanding of simple algebra would be useful.