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Multi Head Attention in Transformer Neural Networks with Code! 1 год назад


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Multi Head Attention in Transformer Neural Networks with Code!

Let's talk about multi-head attention in transformer neural networks Let's understand the intuition, math and code of Self Attention in Transformer Neural Networks ABOUT ME ⭕ Subscribe: https://www.youtube.com/c/CodeEmporiu... 📚 Medium Blog:   / dataemporium   💻 Github: https://github.com/ajhalthor 👔 LinkedIn:   / ajay-halthor-477974bb   RESOURCES [ 1🔎] Code for video: https://github.com/ajhalthor/Transfor... [2 🔎] Transformer Main Paper: https://arxiv.org/abs/1706.03762 [3 🔎] Bidirectional RNN Paper: https://deeplearning.cs.cmu.edu/F20/d... PLAYLISTS FROM MY CHANNEL ⭕ ChatGPT Playlist of all other videos:    • ChatGPT   ⭕ Transformer Neural Networks:    • Natural Language Processing 101   ⭕ Convolutional Neural Networks:    • Convolution Neural Networks   ⭕ The Math You Should Know :    • The Math You Should Know   ⭕ Probability Theory for Machine Learning:    • Probability Theory for Machine Learning   ⭕ Coding Machine Learning:    • Code Machine Learning   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/AdvancedStati... 📕 Bayesian Statistics: https://imp.i384100.net/BayesianStati... 📕 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 TIMSTAMPS 0:00 Introduction 0:33 Transformer Overview 2:32 Multi-head attention theory 4:35 Code Breakdown 13:47 Final Coded Class

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