Introduction to Transformers | Transformers Part 1

Transformers are a powerful class of models in natural language processing and machine learning, revolutionizing various tasks. From attention mechanisms to self-attention, transformers have reshaped the landscape of deep learning.

Introduced by Vaswani et al., transformers use self-attention mechanisms to process input data in parallel, making them highly efficient for tasks like language translation, summarization, and various other sequence-based tasks.

A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks:

https://arxiv.org/abs/2306.07303

Notes: https://learnwith.campusx.in/s/store/courses/YouTube%20Notes

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⌚Time Stamps⌚

00:00 – Intro
01:01 – What is Transformer? / Overview
05:12 – History of Transformer / Research Paper
07:55 – Impact of Transformers in NLP
10:29 – Democratizing AI
13:08 – Multimodal Capability of Transformers
16:28 – Acceleration of Gen AI
19:07 – Unification of Deep Learning
21:09 – Why transformers were created? / Seq-to-Seq Learning with Neural Networks
25:25 – Neural Machine Translation by Jointly Learning to Align and Translate
33:16 – Attention is all you need
39:10 – The Timeline of Transformers
41:42 – The Advantages of Transformers
46:30 – Real World Application of Transformers
47:30 – DALL*E 2
48:20 – AlphaFold by Google Deepmind
49:08 – OpenAI Codex
49:41 – A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks
50:30 – Disadvantages of Transformers
54:40 – The Future of Transformers
59:20 – Outro

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