Transformer Decoder coded from scratch
Channel: CodeEmporium
Date: 2023-03-20

ABOUT ME
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💻 Github: https://github.com/ajhalthor
👔 LinkedIn: https://www.linkedin.com/in/ajay-halthor-477974bb/

RESOURCES
[ 1 🔎] Blowing up the decoder archtecture: https://youtu.be/ekg-hoob0SM
[ 2 🔎]  Code for video: https://github.com/ajhalthor/Transformer-Neural-Network/blob/main/Transformer_Decoder_EXPLAINED!.ipynb

PLAYLISTS FROM MY CHANNEL
⭕ Transformers from scratch playlist: https://www.youtube.com/watch?v=QCJQG4DuHT0&list=PLTl9hO2Oobd97qfWC40gOSU8C0iu0m2l4
⭕ ChatGPT Playlist of all other videos: https://youtube.com/playlist?list=PLTl9hO2Oobd9coYT6XsTraTBo4pL1j4HJ
⭕ Transformer Neural Networks: https://youtube.com/playlist?list=PLTl9hO2Oobd_bzXUpzKMKA3liq2kj6LfE
⭕  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)
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📕 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

TIMESTAMP
0:00 Introduction
1:34 Parameters of Transformer
5:04 Inputs and Outputs of Transformer 
6:11 Masking
7:16 Instantiating Decoder
9:07 Decoder Forward Pass
11:28 Decoder Layer
13:00 Masked Multi Head Self Attention
23:00 Dropout + Layer Normalization
28:09 Multi Head Cross Attention
34:34 Feed Forward, Activation
36:44 Completing the decoder flow