Transformer Encoder in 100 lines of code!

ABOUT ME ⭕ Subscribe: https://www.youtube.com/c/CodeEmporium?sub_confirmation=1 📚 Medium Blog: https://medium.com/@dataemporium 💻 Github: https://github.com/ajhalthor 👔 LinkedIn: https://www.linkedin.com/in/ajay-halthor-477974bb/ RESOURCES [ 1 🔎] Code for Video: https://github.com/ajhalthor/Transformer-Neural-Network/blob/main/Transformer_Encoder_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) 📕 ⭐ 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 TIMESTAMP 0:00 What we will cover 0:53 Introducing Colab 1:24 Word Embeddings and d_model 3:00 What are Attention heads? 3:59 What is Dropout? 4:59 Why batch data? 7:46 How to sentences into the transformer? 9:03 Why feed forward layers in transformer? 9:44 Why Repeating Encoder layers? 11:00 The “Encoder” Class, nn.Module, nn.Sequential 14:38 The “EncoderLayer” Class 17:45 What is Attention: Query, Key, Value vectors 20:03 What is Attention: Matrix Transpose in PyTorch 21:17 What is Attention: Scaling 23:09 What is Attention: Masking 24:53 What is Attention: Softmax 25:42 What is Attention: Value Tensors 26:22 CRUX OF VIDEO: “MultiHeadAttention” Class 36:27 Returning the flow back to “EncoderLayer” Class 37:12 Layer Normalization 43:17 Returning the flow back to “EncoderLayer” Class 43:44 Feed Forward Layers 44:24 Why Activation Functions? 46:03 Finish the Flow of Encoder 48:03 Conclusion & Decoder for next video