90% accuracy resnet-like CNN from scratch for Intel Image Classification dataset WITHOUT transfer learning and with complex metrics.

Explore the first part of our series on sleep stage classification using Python, EEG data, and powerful libraries like Sklearn and MNE. Perfect for data scientists and neuroscience enthusiasts!

1 School of Electronics and Electrical Engineering, Zhengzhou University of Science and Technology, Zhengzhou, China 2 Department of Mechanical and Electrical Engineering, Henan Vocational College of ...

🎛️ Real-Time ML in Unity. RTML Tool Kit is a lightweight, OSC-controllable machine learning framework for Unity, supporting Linear Regression, KNN, and DTW — designed for Mixed Reality and mobile ...

Deep learning has been widely applied to high-dimensional hyperspectral image classification and has achieved significant improvements in classification accuracy. However, most current hyperspectral ...

You like it or not, AI-generated art is here to stay. A lot of people have jumped on this wagon and have started creating AI art. If you are one of them, you may want to organize your art, and ChatGPT ...

Abstract: The key of image recognition and classification based on machine learning is to extract image feature points effectively and classify image features correctly. In this paper, SURF ...

Abstract: Traditional joint sparse representation based hyperspectral classification methods define a local region for each pixel. Through representing the pixels within the local region ...