Learning to learn and compositionality with deep recurrent neural networks

author: Nando de Freitas, Department of Computer Science, University of Oxford
published: Aug. 31, 2016,   recorded: August 2016,   views: 7087
Categories

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Bibliography

Description

Deep neural network representations play an important role in computer vision, speech, computational linguistics, robotics, reinforcement learning and many other data-rich domains. In this talk I will show that learning-to-learn and compositionality are key ingredients for dealing with knowledge transfer so as to solve a wide range of tasks, for dealing with small-data regimes, and for continual learning. I will demonstrate this with three examples: learning learning algorithms, neural programmers and interpreters, and learning communication.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Reviews and comments:

Comment1 Aaron Wicke, July 7, 2022 at 11:16 a.m.:

This makes deep learning an ideal tool for applications such as facial recognition, speech recognition, and machine translation. Overall, off-policy learning is becoming an increasingly important tool for the creative industries and you can visit this https://www.rushessay.com/ site to get essay writing services for your daily tasks. It is able to process vast amounts of data quickly and accurately, making it ideal for tasks such as facial recognition and translation.

Write your own review or comment:

make sure you have javascript enabled or clear this field: