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Stanford CS330: Deep Multi-task and Meta Learning

Stanford,, Fall 2019

Updated On 02 Feb, 19

Overview

Deep learning has achieved remarkable success in supervised and reinforcement learning problems including image classification, speech recognition, and game playing. These models are, however, to a large degree, specialized for the single task they are trained for. This course will cover the setting where there are multiple tasks to be solved. You will explore goal-conditioned reinforcement learning techniques that can increase learning speed of multiple tasks. You will discover how meta-learning methods can be used to learn new tasks quickly. You will learn how leverage the shared structure of a sequence of tasks to enable knowledge transfer. Through this course, you will develop and advance highly-sought after skills in the field of AI.


Includes

Lecture 1: Stanford CS330: Deep Multi-task and Meta Learning | 2020 | Lecture 1 - Intro to Multi-Task Learning

4.1 ( 11 )


Lecture Details

For more information about Stanfords Artificial Intelligence professional and graduate programs visit: https://stanford.io/3w1OYQ0

To follow along with the course, visit:
https://cs330.stanford.edu/

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu​

Chelsea Finn
Computer Science, PhD
Assistant Professor at Stanford University

Karol Hausman
Computer Science, PhD
Research Scientist at Google Brain

00:17 Introductions

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Sam

Excellent course helped me understand topic that i couldn't while attendinfg my college.

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Dembe

Great course. Thank you very much.

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