ABOUT ME
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π Medium Blog: https://medium.com/@dataemporium
π» Github: https://github.com/ajhalthor
π LinkedIn: https://www.linkedin.com/in/ajay-halthor-477974bb/
RESOURCES
[ 1 π] Blowing up the encoder archtecture: https://youtu.be/QwfuoNhjbkI
[ 2 π] Code for building transformers from scratch: https://github.com/ajhalthor/Transformer-Neural-Network/
PLAYLISTS FROM MY CHANNEL
β Transformers from scratch playlist: https://www.youtube.com/watch?v=QCJQG4DuHT0&list=PLTl9hO2Oobd97qfWC40gOSU8C0iu0m2l4
β ChatGPT Playlist of all other videos: https://youtube.com/playlist?list=PLTl9hO2Oobd9coYT6XsTraTBo4pL1j4HJ
β Transformer Neural Networks: https://youtube.com/playlist?list=PLTl9hO2Oobd_bzXUpzKMKA3liq2kj6LfE
β Convolutional Neural Networks: https://youtube.com/playlist?list=PLTl9hO2Oobd9U0XHz62Lw6EgIMkQpfz74
β The Math You Should Know : https://youtube.com/playlist?list=PLTl9hO2Oobd-_5sGLnbgE8Poer1Xjzz4h
β Probability Theory for Machine Learning: https://youtube.com/playlist?list=PLTl9hO2Oobd9bPcq0fj91Jgk_-h1H_W3V
β Coding Machine Learning: https://youtube.com/playlist?list=PLTl9hO2Oobd82vcsOnvCNzxrZOlrz3RiD
MATH COURSES (7 day free trial)
π Mathematics for Machine Learning: https://imp.i384100.net/MathML
π Calculus: https://imp.i384100.net/Calculus
π Statistics for Data Science: https://imp.i384100.net/AdvancedStatistics
π Bayesian Statistics: https://imp.i384100.net/BayesianStatistics
π Linear Algebra: https://imp.i384100.net/LinearAlgebra
π Probability: https://imp.i384100.net/Probability
OTHER RELATED COURSES (7 day free trial)
π β Deep Learning Specialization: https://imp.i384100.net/Deep-Learning
π Python for Everybody: https://imp.i384100.net/python
π MLOps Course: https://imp.i384100.net/MLOps
π Natural Language Processing (NLP): https://imp.i384100.net/NLP
π Machine Learning in Production: https://imp.i384100.net/MLProduction
π Data Science Specialization: https://imp.i384100.net/DataScience
π Tensorflow: https://imp.i384100.net/Tensorflow
TIMESTAMP
0:00 Introduction
2:00 What is the Encoder doing?
3:30 Text Processing
5:05 Why are we batching data?
6:03 Position Encoding
6:34 Query, Key and Value Tensors
7:57 Masked Multi Head Self Attention
15:30 Residual Connections
17:47 Multi Head Cross Attention
21:25 Finishing up the Decoder Layer
22:17 Training the Transformer
24:33 Inference for the Transformer