Linear Transformation in Self Attention | Transformers in Deep Learning | Part 3

In this third video of our Transformer series, we’re diving deep into the concept of Linear Transformations in Self Attention. Linear Transformation is fundamental in Self Attention Mechanism, shaping how inputs are mapped to key, query, and value vectors. In this lesson, we’ll explore the role of linear transformation, breaking down the math behind them to see why they’re essential for capturing dependencies in Self Attention. We’ll go through detailed mathematical proofs to show how Linear Transformation work and why it is crucial for capturing relevant similarities and generate an appropriate word representation that is based on training of the model, in Self Attention Mechanism. If you’re ready to master the theory behind Transformers & Self Attention, hit play, and let’s get started! Don’t forget to like, subscribe, and share if you find this valuable. ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Timestamps: 0:00 Intro 1:31 Recap of Self Attention 9:33 Without Learnable Parameters 14:01 Linear Transformation 15:44 Changing Dimensions 16:34 Feature Extraction with Linear Transformation 18:00 Math of Linear Transformation in Self Attention 22:33 Math of capturing dependencies 25:12 Training the parameters 26:50 Number of parameters 28:37 Outro ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Follow my entire Transformers playlist : 📕 Transformers Playlist:    • Transformers in Deep Learning | Introducti...   ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ ✔ RNN Playlist:    • What is Recurrent Neural Network in Deep L...   ✔ CNN Playlist:    • What is CNN in deep learning? Convolutiona...   ✔ Complete Neural Network:    • How Neural Networks work in Machine Learni...   ✔ Complete Logistic Regression Playlist:    • Logistic Regression Machine Learning Examp...   ✔ Complete Linear Regression Playlist:    • What is Linear Regression in Machine Learn...   ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖