This repository serves as an educational resource for learning the foundational architectures of natural language processing through concise code implementations. It provides a structured collection of deep learning models designed to process and understand human language, focusing on the core mechanics of neural network sequence modeling and text analysis.
The project distinguishes itself by offering direct, hands-on implementations of complex architectures, including Transformers, attention mechanisms, and word embedding generation. By utilizing tensor-based computational graphs and gradient descent, these tutorials demonstrate how to build models capable of sequence prediction, text classification, and language translation.
The instructional material covers a broad range of techniques, from recurrent sequence processing to self-attention mechanisms. These implementations allow users to explore how models map semantic relationships in high-dimensional vector spaces and maintain context across long-range dependencies in text. The repository is organized as a series of Jupyter Notebooks, providing a practical environment for studying and executing these deep learning workflows.
