This repository serves as a comprehensive educational resource for deep learning, focusing on recommendation systems, natural language processing, and model deployment. It targets engineers and researchers seeking practical implementations and theoretical explanations of various deep learning architectures and techniques. The project offers a structured collection of code examples and tutorials, primarily using TensorFlow and PyTorch, to facilitate learning and application.

How It Works

The project is organized into distinct directories, each dedicated to a specific deep learning domain or technique. It provides code implementations for foundational concepts like TensorFlow batch normalization, TFRecord data handling, and custom operator development. Advanced topics such as multi-task learning (MTL) with models like MMoE and PLE, and various recommendation system architectures (e.g., MIND, ComiRec, DIN, DIEN, STAR, SAR-Net) are covered. The repository also includes sections on NLP with BERT variants, deep learning tricks, multimodal AI (Stable Diffusion), embedding compression, and large language models (LLMs) with LangChain.

Quick Start & Requirements

• Installation: Primarily involves cloning the repository and setting up Python environments. Specific sub-projects may have their own dependencies.
• Prerequisites: Python 3.x, TensorFlow (versions vary, some examples use TF 1.x), PyTorch, and potentially libraries like Hugging Face Transformers, TensorRT, and LangChain. GPU acceleration is highly recommended for most deep learning tasks.
• Resources: Deep learning tasks, especially training, can be computationally intensive, requiring significant GPU memory and processing power.

Highlighted Details

• Extensive coverage of recommendation system architectures, including matching (recall) and ranking (prediction) models.
• Detailed explanations and implementations of various NLP models, with a focus on BERT and its applications.
• Practical guides on model deployment using TensorFlow Serving, TensorRT, and Triton Inference Server.
• Introduction to multimodal AI with Stable Diffusion and LLM development using LangChain.

Maintenance & Community

The repository appears to be a personal project, with content organized and explained through associated "专栏" (columns/articles), likely blog posts or tutorials. Specific community channels or active development contributions are not explicitly detailed in the README.

Licensing & Compatibility

The README does not specify a license. The code examples are presented for educational purposes, and commercial use or integration into closed-source projects would require clarification of licensing terms.

Limitations & Caveats

Some code examples are noted to use TensorFlow 1.x, which may require specific environment setups or migration to TensorFlow 2.x for compatibility with newer libraries. The project's primary focus is on educational content and code examples rather than a production-ready library.