floodsung/Deep-Learning-Papers-Reading-Roadmap

Project overview

This project is a structured reading roadmap that guides newcomers through deep learning papers, organized from broad foundations to specialized and state-of-the-art topics. Readers select papers based on their own interests and research direction to build an understanding of deep learning history, CNNs, RNNs, LSTM, and recognition applications.

Use cases

Learning & Education

Deployment

Refer to project documentation

License

License pending

Best for

• General users and researchers looking for a topic-based path to select deep learning papers according to their specific interests.

Key capabilities

• Organizes deep learning reading from outline to detail, older work to state-of-the-art work, and generic material to specific areas.
• Provides papers covering deep learning methods, application areas, and research frontiers that readers can choose according to their interests and research direction.

Limitations and risks

• GPU requirements, operating systems, minimum hardware, coding requirements, telemetry, and cost dependencies are not documented.

Getting started

• The setup process, difficulty, and first success path are not documented.

Evidence and sources

• GitHub project description: Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
• README: If you are a newcomer to the Deep Learning area, the first question you may have is "Which paper should I start reading from?" >Here is a reading roadmap of Deep Learning papers!
• README: The roadmap is constructed in accordance with the following four guidelines: - From outline to detail - From old to state-of-the-art - from generic to specific areas - focus on st…
• README: The following papers will take you in-depth understanding of the Deep Learning method, Deep Learning in different areas of application and the frontiers. I suggest that you can ch…
• README: After reading above papers, you will have a basic understanding of the Deep Learning history, the basic architectures of Deep Learning model(including CNN, RNN, LSTM) and how deep…

Reviewed at: 2026-07-17T08:45:56Z