Architecture

Overview The Transformer architecture, introduced in “Attention Is All You Need” (2017), revolutionized NLP and now powers all modern LLMs. Key Components Input → Embedding → Positional Encoding → Transformer Blocks → Output ↓ [Multi-Head Attention + FFN] × N Self-Attention The core mechanism that allows tokens to attend to all other tokens. import torch import torch.nn.functional as F def attention(Q, K, V, mask=None): d_k = Q.size(-1) scores = torch.matmul(Q, K.transpose(-2, -1)) / (d_k ** 0.5) if mask is not None: scores = scores.masked_fill(mask == 0, -1e9) weights = F.softmax(scores, dim=-1) return torch.matmul(weights, V) Multi-Head Attention Run attention in parallel with different learned projections: ...

Attention Mechanism Definition A technique that allows neural networks to focus on specific parts of the input data while processing it. Detailed Explanation In the world of Architecture, Attention Mechanism is defined as a technique that allows neural networks to focus on specific parts of the input data while processing it. At its core, Attention Mechanism solves a specific problem in the AI landscape. Unlike traditional approaches, it leverages advanced algorithms to process data more efficiently. ...

Generative Adversarial Network Definition A class of machine learning frameworks designed by Ian Goodfellow and his colleagues in 2014. Last updated: February 2026

Parameter Definition Internal variables of the model that are learned during training (weights and biases). Detailed Explanation Parameter is a fundamental concept in Architecture that refers to internal variables of the model that are learned during training (weights and biases). The significance of Parameter cannot be overstated. As AI systems become more complex, mechanisms like this ensure scalability and accuracy. Applications of ParameterReal-world applications include advanced natural language processing, computer vision systems, and automated decision-making frameworks. ...

Transformer Definition A deep learning architecture introduced by Google in 2017 that uses self-attention mechanisms to weigh the significance of each part of the input data. Detailed Explanation Transformer is a fundamental concept in Architecture that refers to a deep learning architecture introduced by google in 2017 that uses self-attention mechanisms to weigh the significance of each part of the input data. The significance of Transformer cannot be overstated. As AI systems become more complex, mechanisms like this ensure scalability and accuracy. ...