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

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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