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Explore Transformer Neural Networks - EXPLAINED! (Attention is all you need) with 885,578 views, 27,021 likes, and 717 comments. Experience the impact of this video content that has captured audience attention.

Please subscribe to keep me alive: https://www.youtube.com/c/CodeEmporium?sub_confirmation=1 BLOG: https://medium.com/@dataemporium PLAYLISTS FROM MY CHANNEL ⭕ Reinforcement Learning: https://youtube.com/playlist?list=PLTl9hO2Oobd9kS--NgVz0EPNyEmygV1Ha&si=AuThDZJwG19cgTA8 Natural Language Processing: https://youtube.com/playlist?list=PLTl9hO2Oobd_bzXUpzKMKA3liq2kj6LfE&si=LsVy8RDPu8jeO-cc ⭕ Transformers from Scratch: https://youtube.com/playlist?list=PLTl9hO2Oobd_bzXUpzKMKA3liq2kj6LfE ⭕ ChatGPT Playlist: https://youtube.com/playlist?list=PLTl9hO2Oobd9coYT6XsTraTBo4pL1j4HJ ⭕ Convolutional Neural Networks: https://youtube.com/playlist?list=PLTl9hO2Oobd9U0XHz62Lw6EgIMkQpfz74 ⭕ The Math You Should Know : https://youtube.com/playlist?list=PLTl9hO2Oobd-_5sGLnbgE8Poer1Xjzz4h ⭕ Probability Theory for Machine Learning: https://youtube.com/playlist?list=PLTl9hO2Oobd9bPcq0fj91Jgk_-h1H_W3V ⭕ Coding Machine Learning: https://youtube.com/playlist?list=PLTl9hO2Oobd82vcsOnvCNzxrZOlrz3RiD MATH COURSES (7 day free trial) 📕 Mathematics for Machine Learning: https://imp.i384100.net/MathML 📕 Calculus: https://imp.i384100.net/Calculus 📕 Statistics for Data Science: https://imp.i384100.net/AdvancedStatistics 📕 Bayesian Statistics: https://imp.i384100.net/BayesianStatistics 📕 Linear Algebra: https://imp.i384100.net/LinearAlgebra 📕 Probability: https://imp.i384100.net/Probability OTHER RELATED COURSES (7 day free trial) 📕 ⭐ Deep Learning Specialization: https://imp.i384100.net/Deep-Learning 📕 Python for Everybody: https://imp.i384100.net/python 📕 MLOps Course: https://imp.i384100.net/MLOps 📕 Natural Language Processing (NLP): https://imp.i384100.net/NLP 📕 Machine Learning in Production: https://imp.i384100.net/MLProduction 📕 Data Science Specialization: https://imp.i384100.net/DataScience 📕 Tensorflow: https://imp.i384100.net/Tensorflow REFERENCES [1] The main Paper: https://arxiv.org/abs/1706.03762 [2] Tensor2Tensor has some code with a tutorial: https://www.tensorflow.org/tutorials/text/transformer [3] Transformer very intuitively explained - Amazing: http://jalammar.github.io/illustrated-transformer/ [4] Medium Blog on intuitive explanation: https://medium.com/inside-machine-learning/what-is-a-transformer-d07dd1fbec04 [5] Pretrained word embeddings: https://nlp.stanford.edu/projects/glove/ [6] Intuitive explanation of Layer normalization: https://mlexplained.com/2018/11/30/an-overview-of-normalization-methods-in-deep-learning/ [7] Paper that gives even better results than transformers (Pervasive Attention): https://arxiv.org/abs/1808.03867 [8] BERT uses transformers to pretrain neural nets for common NLP tasks. : https://ai.googleblog.com/2018/11/open-sourcing-bert-state-of-art-pre.html [9] Stanford Lecture on RNN: http://cs231n.stanford.edu/slides/2018/cs231n_2018_lecture10.pdf [10] Colah’s Blog: https://colah.github.io/posts/2015-08-Understanding-LSTMs/ [11] Wiki for timeseries of events: https://en.wikipedia.org/wiki/Transformer_(machine_learning_model)

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