This page provides a curated list of technical papers and resources related to machine learning. Explore foundational theories, cutting-edge advancements, and practical applications through the following categories:

📚 Classic Papers

  • "A Probabilistic Model for Matching" by Judea Pearl (1988)

    probabilistic_model
    *Lays the groundwork for probabilistic reasoning in AI systems.*

  • "Neural Networks and Deep Learning" by Michael Nielsen

    neural_networks
    *A beginner-friendly introduction to deep learning concepts.*

🔬 Latest Research

  • "Efficient Attention: A Survey" (2023)

    attention_mechanisms
    *Explores advancements in attention mechanisms for transformers.*

  • "Large Language Models: A Technical Overview" (2024)

    large_language_models
    *Dives into the architecture and training of modern LLMs.*

🌍 Applications in Industry

  • Image Recognition

    image_recognition
    *Papers on CNNs and vision transformers.*

  • Natural Language Processing

    nlp_techniques
    *Resources on BERT, GPT, and sequence modeling.*

For further reading, check our AI Research Hub for interdisciplinary studies.