This week we’re looking into transformers. Transformers were introduced a couple of years ago with the paper Attention is All You Need by Google Researchers. Since its introduction transformers has been widely adopted in the industry. Get your Free Token for AssemblyAI Speech-To-Text API 👇 https://www.assemblyai.com/?utm_source=youtube&utm_medium=referral&utm_campaign=yt_mis_8&utm_id=yt_mis_8 Models like BERT, GPT-3 made groundbreaking improvements in the world of NLP using transformers. Since then model libraries like hugging face made it possible for everyone to use transformer based models in their projects. But what are transformers and how do they work? How are they different from other deep learning models like RNNs, LSTMs? Why are they better? In this video, we learn about it all! Some of my favorite resources on Transformers: The original paper - https://arxiv.org/pdf/1706.03762.pdf If you’re interested in following the original paper with the code - http://nlp.seas.harvard.edu/2018/04/03/attention.html The Illustrated Transformer – https://jalammar.github.io/illustrated-transformer/ Blog about positional encodings - https://kazemnejad.com/blog/transformer_architecture_positional_encoding/ About attention - Visualizing A Neural Machine Translation Model - https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/ Layer normalization - https://arxiv.org/abs/1607.06450 Some images used in this video are from: https://colah.github.io/posts/2015-08-Understanding-LSTMs/ https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/ https://medium.com/nanonets/how-to-easily-build-a-dog-breed-image-classification-model-2fd214419cde https://medium.com/swlh/elegant-intuitions-behind-positional-encodings-dc48b4a4a5d1