100 Days of Deep Learning - 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 ============================ Do you want to learn from me? Check my affordable mentorship program at : https://learnwith.campusx.in/s/store ============================ 📱 Grow with us: CampusX' LinkedIn: https://www.linkedin.com/company/campusx-official CampusX on Instagram for daily tips: https://www.instagram.com/campusx.official My LinkedIn: https://www.linkedin.com/in/nitish-singh-03412789 Discord: https://discord.gg/PsWu8R87Z8 E-mail us at [email protected] ✨ Hashtags✨ #Transformers #NLP #MachineLearning #deeplearning ⌚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