Self-Attention Mechanism
Attention Mechanisms DS practice problem on Onlearn.
Difficulty: medium.
Topics: Understanding Implement Self-Attention Mechanism, Scaled Dot-Product Attention, Softmax Normalization, Query-Key-Value Projections, Positional Encoding, Multi-Head Parallelization, Linear Algebra, Deep Learning Architectures, Natural Language Processing, Probability and Statistics, Computational Complexity, Matrix Decompositions, Sequence Modeling, Vector Space Embeddings, Optimization Algorithms, Tensor Operations.
Task: Implement the Self Attention Mechanism Your task is to implement the self attention mechanism, which is a fundamental component of transformer models, widely used in natural language processing and computer vision tasks. The self attention mechanism allows a model to dynamically focus on different parts of the input sequence when generating a contextualized representation. Your function should return the self attention output as a numpy array.