Transformers and Positional Embedding: A Step-by-Step NLP Tutorial for Mastery

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🚀 Introduction to Transformers in NLP | PositionalEmbedding Layer 🚀

Discover the powerful world of Transformers in Natural Language Processing (NLP)! In this tutorial, we explore the main components of the Transformer architecture, with a focus on the essential "PositionalEmbedding" layer.

🎯 Learn the Advantages and Limitations of Transformers.
🎓 Implement Positional Encoding and Understand its Significance.
💡 Build a Custom "PositionalEmbedding" Layer in TensorFlow.
💻 Test the Layer with Random Input Sequences.

Unleash the potential of Transformers in NLP and level up your machine learning skills. Subscribe now for more exciting tutorials on building practical Transformer models for real-world NLP tasks! 🌟

#Transformers #NLP #MachineLearning #Tutorial #PositionalEmbedding #TensorFlow
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Hello Sir, where can I find the jupyter notebook you showed in this video? It is on your Github as well?

breakingBro
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P E(pos, 2i) = sin(pos/100002i/dmodel )
P E(pos, 2i+1) = cos(pos/100002i/dmodel )

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