Lecture 20 - Transformers and Attention Published 2022-12-01 Download video MP4 360p Recommendations 36:16 The math behind Attention: Keys, Queries, and Values matrices 1:08:38 Transformers in Vision: From Zero to Hero 54:39 Rethinking Attention with Performers (Paper Explained) 1:10:48 Lecture 18 - Sequence Modeling and Recurrent Networks 56:20 Transformers with Lucas Beyer, Google Brain 36:15 Transformer Neural Networks, ChatGPT's foundation, Clearly Explained!!! 1:22:38 CS480/680 Lecture 19: Attention and Transformer Networks 58:12 MIT Introduction to Deep Learning | 6.S191 1:58:38 Geoffrey Hinton | Will digital intelligence replace biological intelligence? 1:56:20 Let's build GPT: from scratch, in code, spelled out. 1:19:30 Lecture 9 - Normalization and Regularization 58:04 Attention is all you need (Transformer) - Model explanation (including math), Inference and Training 39:24 Intuition Behind Self-Attention Mechanism in Transformer Networks 56:33 MLBBQ: “Are Transformers Effective for Time Series Forecasting?” by Joanne Wardell 21:02 The Attention Mechanism in Large Language Models 1:11:41 Stanford CS25: V2 I Introduction to Transformers w/ Andrej Karpathy 44:20 Lecture - 12 GPU Acceleration 2:09:12 Seq. 08 / Transformers 57:10 Pytorch Transformers from Scratch (Attention is all you need) 57:55 Lecture 1 - Introduction and Logistics Similar videos 1:18:05 Lecture 20 - Efficient Transformers | MIT 6.S965 13:58 NLP Lecture 20|| Transformers || Question Answering || Causal/Self Attention 1:02:50 MIT 6.S191: Recurrent Neural Networks, Transformers, and Attention 27:14 But what is a GPT? Visual intro to Transformers | Chapter 5, Deep Learning 22:04 CPSC 393 || Lecture 20 Transformers II 15:02 Self Attention in Transformer Neural Networks (with Code!) 56:07 14 Attention & Transformers - Machine Learning - Winter Term 20/21 - Freie Universität Berlin 45:54 Lecture 21 - Transformers - three types of attention - BYU CS 474 Deep Learning 48:21 Synthesizer: Rethinking Self-Attention in Transformer Models (Paper Explained) 48:06 Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention (Paper Explained) More results