This repository serves as a curated collection of resources, research papers, and foundational concepts for Deep Learning applied to Natural Language Processing (NLP). It targets researchers, engineers, and students interested in state-of-the-art NLP tasks like machine translation, sentiment analysis, and conversational AI, providing a structured overview of key advancements and methodologies.

How It Works

The project organizes resources by NLP task and underlying deep learning techniques. It highlights seminal papers and practical implementations for word embeddings (Word2Vec, GloVe, FastText), sentence/document representations (Paragraph Vectors, Tree LSTMs), sequence-to-sequence models for machine translation (RNN Encoder-Decoder, Attention mechanisms, Transformers), and dialogue systems (Seq2Seq, HRED, GANs for dialogue). The emphasis is on understanding the evolution of models and their performance characteristics.

Quick Start & Requirements

This repository is a collection of links and summaries, not a runnable codebase. It requires no installation but assumes familiarity with deep learning concepts and Python. Links to source code, datasets, and tutorials are provided within the text for specific implementations.

Highlighted Details

• Comprehensive coverage of word embedding techniques, including Word2Vec, GloVe, and FastText, with explanations of their underlying principles and optimizations.
• Detailed overview of Neural Machine Translation (NMT) advancements, from early RNN encoder-decoder models to attention mechanisms and the Transformer architecture.
• Exploration of various approaches to dialogue systems, including retrieval-based and generative models, with a focus on sequence-to-sequence frameworks and reinforcement learning.
• Inclusion of foundational papers on memory and attention mechanisms, such as Neural Turing Machines and Memory Networks.

Maintenance & Community

This is a personal collection of resources, with no explicit mention of active maintenance or community channels. The content appears to be a snapshot of research up to early 2019.

Licensing & Compatibility

The repository itself is not licensed. The linked papers and code repositories are subject to their respective licenses. Users must consult the licenses of any external code or data they choose to use.

Limitations & Caveats

The content is a curated list of papers and concepts, not a unified framework or library. It reflects the state of NLP research as of early 2019 and may not include the most recent advancements. Some linked resources might be outdated or have broken links.