Attention in transformers, visually explained | DL6

This one is a bit more symbol-heavy, and that’s actually the point. The goal here is to represent in somewhat more formal terms the intuition for how backpropagation works in part 3 of the series, hopefully providing some connection between that video and other texts/code that you come across later. For more on backpropagation:http://neuralnetworksanddeeplearning….https://github.com/mnielsen/neural-ne…http://colah.github.io/posts/2015-08-… https://colah.github.io/posts/2015-08-Backprop

Topics: Overview of course, OptimizationPercy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor – Stanford Universityhttp://onlinehub.stanford.edu/ Associate Professor Percy LiangAssociate Professor of Computer Science and Statistics (courtesy) Assistant Professor Dorsa SadighAssistant Professor in the Computer Science Department & Electrical Engineering Department To follow along with the course schedule and syllabus, visit:https://stanford-cs221.github.io/autumn2019/#schedule artificialintelligencecourse 0:00 Introduction3:30 Why…

For more information about Stanford’s Artificial Intelligence programs visit: https://stanford.io/ai https://www.youtube.com/watch?v=Bl4Feh_Mjvo To follow along with the course, visit:https://cs229.stanford.edu/syllabus-s… Tengyu MaAssistant Professor of Computer Sciencehttps://ai.stanford.edu/~tengyuma/ Christopher RéAssociate Professor of Computer Sciencehttps://cs.stanford.edu/~chrismre/

This video on the Artificial Intelligence tutorial will make you learn in detail about the different concepts involved in AI. You will understand the basics of AI and get an idea about Machine Learning and Deep Learning with hands-on demo in this Artificial Intelligence full course. You will look at how to become an AI…

Timestamps:0:00 – Who this was made for0:41 – What are large language models?7:48 – Where to learn more

The attention mechanism is well known for its use in Transformers. But where does it come from? It’s origins lie in fixing a strange problems of RNNs. Chapters0:00 Introduction0:22 Machine Translation2:01 Attention Mechanism8:04 Outro