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dmurfet / talk-s2-2019.md
Last active Aug 2, 2019
Plan for 2019 S2 seminar
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S2 2019

According to the history of logic in the Encyclopaedia Britannica, logic emerged from the study of philosophical arguments, and the realisation that were general patterns by which one could distinguish valid and invalid forms of argumentation. The systematic study of logic was begun by Aristotle, who established a system of formal rules and strategy for reasoning. The use of the word strategy is intentional:

The practice of such techniques in Aristotle’s day was actually competitive, and Aristotle was especially interested in strategies that could be used to “win” such “games.” Naturally, the ability to predict the “answer” that a certain line of questioning would yield represented an important advantage in such competitions. Aristotle noticed that in some cases the answer is completely predictable—viz., when it is (in modern terminology) a logical consequence of earlier answers. Thus, he was led from the study of interrogative techniques to

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dmurfet / dlaus.md
Last active Jul 30, 2020
Deep Learning in Australia
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Deep Learning in Australia

This document has moved to its own page: antipode.ai and is no longer being updated on Github gists.

View mdlg-teaching.md

MDLG learning community

The Melbourne Deep Learning Group (MDLG) is in the first place a research group, but given the broader importance of these technologies and urgency of Australia adopting them, we also take on a responsibility for helping to educate students at the University of Melbourne, and the broader Australian community. There are many free or low-cost introductory courses on deep learning, e.g. deeplearning.ai and fast.ai and there is no point in us reproducing some slight variation on this content. However, while there is plenty of content available, that doesn't mean it is trivial to learn in a vacuum (that's what classes are for!).

We therefore focus our attention on facilitating a thriving local community and runnning short events that help to motivate members of this community to deepen their understanding of these technologies and their applications, and to meet collaborators (e.g. we

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dmurfet / dlapplied.md
Last active Apr 20, 2020
Commercial applications of deep learning
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Why is deep learning important?

It is still unclear what the long-term impacts of this technology will be. Large changes in productivity have occurred in history, and the potential of deep learning is comparable to other general purpose technologies (steam, electricity, chemical manufacturing, etc) responsible for those changes. While there are many real-world applications of today's deep learning in computer vision, natural language, and perhaps soon in robotics, these impacts would have to increase by several orders of magnitude to be reasonably compared with the general purpose technologies which drove previous industrial revolutions. However, as anybody familiar with the history of the industrial revolutions knows, once it is obvious to everybody that things are working you may not have time to catch up.

It is therefore worth noting that rich governments (US, China) and corporations (Google, Facebook, Amazon, Microsoft, Baidu, Alib