Attention in transformers, visually explained | DL6

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…

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

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

How does AI learn? Is AI conscious & sentient? Can AI break encryption? How does GPT & image generation work? What’s a neural network? #ai #agi #qstar #singularity #gpt #imagegeneration #stablediffusion #humanoid #neuralnetworks #deeplearning

To learn more, I highly recommend the book by Michael Nielsenhttp://neuralnetworksanddeeplearning….The book walks through the code behind the example in these videos, which you can find here:https://github.com/mnielsen/neural-ne… MNIST database:http://yann.lecun.com/exdb/mnist/ Also check out Chris Olah’s blog:http://colah.github.io/His post on Neural networks and topology is particular beautiful, but honestly all of the stuff there is great. And if…

This is the second of a series of 3 videos where we demystify Transformer models and explain them with visuals and friendly examples. 00:00 Introduction01:18 Recap: Embeddings and Context04:46 Similarity11:09 Attention20:46 The Keys and Queries Matrices25:02 The Values Matrix28:41 Self and Multi-head attention33:54: Conclusion