This section provides a comprehensive collection of research papers related to our field of study. Below is a curated list of papers that delve into various aspects of our research.
Recent Papers
- Deep Learning in Image Recognition
- This paper discusses the application of deep learning techniques in image recognition tasks. It includes a detailed analysis of convolutional neural networks and their performance on different datasets.
Classic Papers
- The perceptron: A permutation learning device
- A foundational paper by Frank Rosenblatt, this work introduces the concept of the perceptron and its applications in linear classification.
Useful Resources
- Neural Networks and Deep Learning
- This resource provides an excellent introduction to neural networks and deep learning, covering the basics and advanced topics.
Convolutional Neural Network
Further Reading
For more in-depth information and resources, please visit our Documentation section.