An Introduction to AI Unit 4: Convolutional and Recurrent Neural Networks
This unit will explore some more advanced types of neural networks. We will look at convolutional neural nets, which are really good for image processing such as recognising faces and animals. For sequence type data, such as language and speech, we will look at using recurrent neural networks and memory models. We will also explore reinforcement learning, where systems obtain and learn from data from the environment and the use of the AI system itself. This unit will also discuss some applications and how one can start from pre-trained systems and adapt them for other applications, so-called transfer learning.
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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.
• Understand more advanced types of neural networks.
• Have gained enough background to run the programs we discuss in this unit or use other open source libraries you might find on the internet.
• Understand convolutional neural networks.
• Understand recurrent neural networks.
• Understand transfer learning using Keras.