This repository serves as a curated collection of notes and code implementations for deep learning experiments, primarily targeting non-specialist NLP and image engineers who prioritize rapid prototyping. It aims to provide accessible introductions to deep learning concepts, practical code examples, and novel research directions for those looking to quickly get started or explore new ideas in the field.

How It Works

The project offers a modular approach, with distinct sub-directories for various deep learning models and techniques. It emphasizes practical application and ease of use, often providing corrected or runnable versions of existing research code. The implementations cover a range of tasks including image recognition (SSD), recommendation systems (DeepFM, DNN for YouTube Recommendations, DeepInterestNetwork), and NLP tasks (TextCNN, BERT fine-tuning, Doc2Vec). The use of TensorFlow is prevalent, with a notable shift towards the Estimator API for more standardized development.

Quick Start & Requirements

• Installation: pip install -r requirements.txt
• Prerequisites: TensorFlow (version 1.6.0 recommended to avoid Linux-specific double free or corruption errors with v1.0.0), Python. Specific models may have additional dependencies detailed within their respective directories.
• Resources: No explicit GPU or CUDA requirements are stated, but deep learning tasks generally benefit from GPU acceleration. Setup time is likely minimal for basic usage, but training complex models will require significant time and resources.
• Links: No direct links to official docs or demos are provided within the README.

Highlighted Details

• Provides runnable code for Google's "Deep Neural Networks for YouTube Recommendations" with added attention mechanisms.
• Offers a corrected and functional implementation of the Wide & Deep model.
• Includes a TextCNN implementation that evolved from simpler models to incorporate Doc2Vec, CBOW, and GLOVE, achieving high recall and precision.
• Demonstrates BERT fine-tuning for Named Entity Recognition (NER) and classification tasks.

Maintenance & Community

• The project appears to be a personal collection, with no explicit mention of maintainers, community channels (like Discord/Slack), or a public roadmap. Contact is via email: stw386@sina.com.

Licensing & Compatibility

• The README does not specify a license. The presence of code derived from various sources implies potential licensing complexities for commercial use or closed-source integration.

Limitations & Caveats

The project is presented as a personal research summary and may not adhere to strict software engineering best practices. Some implementations are noted as incomplete or still under development ("实在没空写博客了，sorry"). The TensorFlow version dependency for avoiding specific errors is a notable caveat.