Transformers from scratch Blowing up Transformer Decoder architecture
Blowing up Transformer Decoder architecture Transcript and Lesson Notes
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Quick Summary
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Key Takeaways
- Review the core idea: 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-halt
- Understand how Machine Learning fits into Blowing up Transformer Decoder architecture.
- Understand how Deep Learning fits into Blowing up Transformer Decoder architecture.
- Understand how Data Science fits into Blowing up Transformer Decoder architecture.
- Understand how Artificial Intelligence fits into Blowing up Transformer Decoder architecture.
Key Concepts
Full Transcript
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 🔎] Blowing up the encoder archtecture: https://youtu.be/QwfuoNhjbkI [ 2 🔎] Code for building transformers from scratch: https://github.com/ajhalthor/Transformer-Neural-Network/ 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 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
Lesson FAQs
What is Blowing up Transformer Decoder architecture about?
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-halt
What key concepts are covered in this lesson?
The lesson covers Machine Learning, Deep Learning, Data Science, Artificial Intelligence, Neural Network.
What should I learn before Blowing up Transformer Decoder architecture?
Review the previous lessons in Transformers from scratch, then use the transcript and key concepts on this page to fill any gaps.
How can I practice after this lesson?
Practice by applying the main concepts: Machine Learning, Deep Learning, Data Science, Artificial Intelligence.
Does this lesson include a transcript?
Yes. The full transcript is visible on this page in indexable HTML sections.
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