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.

Text tutorials and sample code: https://pythonprogramming.net/....balancing-rnn-data-d

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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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Relevant URLs
Richard Sutton book: https://webdocs.cs.ualberta.ca..../~sutton/book/ebook/
Tambet Matiisen post: https://www.nervanasys.com/dem....ystifying-deep-reinf
Andrej Karpathy post: http://karpathy.github.io/2016/05/31/rl/

Credits
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelan....cers/~0147b8991909b2
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Marek Scibior (Prezi creator, Illustrator) -
http://brawuroweprezentacje.pl/
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

Geometric Deep Learning is able to draw insights from graph data. That includes social networks, sensor networks, the entire Internet, and even 3D Objects (if we consider point cloud data to be a graph). I'll explain how it works via a demo of me using a graph convolutional network to classify people by their interest in sports teams as well as a 3D object classification demo. At its core, it comes down to being able to learn from non-Euclidean data. Euclid's laws help define certain types of data, so I'll cover some geometry background as well. Enjoy!

Code for this video:
https://github.com/llSourcell/pytorch_geometric

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More learning resources:
http://sungsoo.github.io/2018/....02/01/geometric-deep
http://geometricdeeplearning.com/
https://arxiv.org/abs/1611.08097
http://3ddl.stanford.edu/CVPR1....7_Tutorial_Intrinsic
https://github.com/rusty1s/pytorch_geometric
https://pemami4911.github.io/p....aper-summaries/deep-

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