This repository is a curated collection of significant deep learning research papers, organized by topic and including scratch implementations. It aims to provide a structured learning resource for researchers and practitioners to understand the evolution and core concepts of deep learning.

How It Works

The project meticulously categorizes influential papers across various deep learning domains, such as foundational networks, optimization, sequence modeling, language models, generative models, and reinforcement learning. For each paper, it provides links to the original publication and offers custom implementations to facilitate hands-on learning and comprehension of the underlying algorithms and architectures.

Quick Start & Requirements

• Install: No explicit installation instructions are provided. The project appears to be a collection of code and documentation.
• Prerequisites: Python environment, deep learning libraries (e.g., TensorFlow, PyTorch, NumPy), and potentially specific hardware (GPU) for running implementations.
• Resources: Running the implementations will require computational resources depending on the complexity of the models.

Highlighted Details

• Comprehensive coverage of over 60 key deep learning papers.
• Includes implementations for foundational models like DNN, CNN, LeNet, AlexNet, and U-Net.
• Features modern advancements in language modeling (Transformer, BERT, GPT series) and generative models (GAN, VAE, Diffusion Models).
• Covers deep reinforcement learning, optimization techniques, and inference optimizations like FlashAttention.

Maintenance & Community

This is a personal learning project, with implementations and notes potentially containing errors. The project is inspired by other repositories and expanded upon. No specific community channels or maintenance details are provided.

Licensing & Compatibility

The repository does not specify a license. The code and content are for personal learning and should be used with caution, referring to original papers.

Limitations & Caveats

As a personal learning project, the implementations may contain errors or simplifications. The lack of a specified license and clear community support might impact collaborative development or commercial use.