Our next ML study group meeting will take place on Monday the 8^th of October.   I'll cover the contraction theorem.   See relevant section in https://nl.overleaf.com/read/dsmkpzdbbshq  (section 7.1) for slide and lecture notes.   The contraction map theorem is at the heart of Markov Decision Processes, Reinforcement Learning and the existence of a Nash equilibrium.  

Also, we are reigniting the ML study group.  In the next session Ora will cover Gradient Decent which is fundamental to the understanding of deep neural networks and transformer training.  

 Classical Hopfield update operation convergence meeting recording -  https://www.youtube.com/watch?v=zkV8zjVqFoI

 A youtube on the attention is all you need paper -

https://www.youtube.com/watch?v=XowwKOAWYoQ

A youtube on the Hopfield is all you need paper -

Hopfield Networks is All You Need (Paper Explained) - YouTube

 We will cover the update process of classical Hopfield networks.   https://drive.google.com/file/d/16wVcMXIJrn576Guop8zfPv7hzEL8pT8D/view?usp=share_link

 Tomorow we will discuss how backpropagation is done locally at a gate.  This enables a lot of the DNN training methods.   https://drive.google.com/file/d/1Y598WVObk5MYFKLxL0YXPK6qGtt1oju_/view?usp=share_link

 We will start discussing DNN and transformers.  See link.   Recommended book on the topic link.

 How can ERM be fooled - How can ERM be fooled? - YouTube