Artificial intelligence (AI) is a branch of engineering that has traditionally ignored brains, but recent advances in biologically-inspired deep learning have dramatically changed AI and made it possible to solve difficult problems in vision, planning and natural language. If you talk to Alexa or use Google Translate, you have experienced deep learning in action. This new technology opens a Pandora's box of problems that we must confront regarding privacy, bias and jobs. Terry Sejnowski, PhD, explains how his research strives to understand the computational resources of brains and to build linking principles from brain to behavior using computational models. [6/2020] [Show ID: 35462]

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Welcome to the next part of our Deep Learning with Python, TensorFlow, and Keras tutorial series. In this tutorial, we're going to continue building our cryptocurrency-price-predicting Recurrent Neural Network.

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Machine learning using neural networks is a very powerful methodology which has demonstrated utility in many different situations. In this talk I will show how work in the mathematical discipline called topological data analysis can be used to (1) lessen the amount of data needed in order to be able to learn and (2) make the computations more transparent. We will work primarily with image and video data.

This talk was part of the workshop on "Topological Data Analysis - Theory and Applications" supported by the Tutte Institute and Western University: https://math.sci.uwo.ca/~jardine/TDA-2021.html

Reinforcement Learning has started to receive a lot of attention in the fields of Machine Learning and Data science. In January of 2016, a team of researchers from Google built an AI that beat the reigning world champion of the board game Go. This AI, AlphaGo, utilizes reinforcement learning in order to discover new strategies. Despite the potential of reinforcement learning, there are very few learning resources currently available. This video will help to demystify the field so that its capabilities can be better understood.

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Richard Sutton book: https://webdocs.cs.ualberta.ca..../~sutton/book/ebook/
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Andrej Karpathy post: http://karpathy.github.io/2016/05/31/rl/

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