Good information, but the narrator sounds like she's telling her friend about "like super cool party from last night" instead of explainning and emphasising important points. Sounds like she just read something without understanding it, which makes it for me extremely difficult to understand. thanks for the video though.
@MINGJIPHD
4 ай бұрын
Hi, Thanks for your feedback! Yes, this video is informational. It is for increasing the awareness of the random projection method. In the future, I will make videos to explain its technical details including how to videos showing its implementation. Stay tuned. 🙂
@MINGJIPHD
4 ай бұрын
Here's a list of papers and online courses related to learning about random projection: Papers: Achlioptas, D. (2001). Database-friendly random projections: Johnson-Lindenstrauss with binary coins. Journal of Computer and System Sciences, 66(4), 671-687. Dasgupta, S., & Gupta, A. (2003). An elementary proof of a theorem of Johnson and Lindenstrauss. Random Structures & Algorithms, 22(1), 60-65. Li, P., Hastie, T. J., & Church, K. W. (2006). Very sparse random projections. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 287-296). Clarkson, K. L., & Woodruff, D. P. (2009). Numerical linear algebra in the streaming model. In Proceedings of the 41st annual ACM symposium on Theory of Computing (pp. 205-214). Bingham, E., & Mannila, H. (2001). Random projection in dimensionality reduction: Applications to image and text data. In Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 245-250). Online Courses: Coursera: "Dimensionality Reduction in Machine Learning" by University of Washington - This course covers various dimensionality reduction techniques including random projection and their applications in machine learning. Udemy: "Random Projections for Data Science and Machine Learning" - This course provides a comprehensive introduction to random projection techniques, their theoretical foundations, and practical implementation in data science and machine learning tasks. edX: "High-Dimensional Data Analysis" by Harvard University - This course covers advanced topics in high-dimensional data analysis, including random projection techniques for dimensionality reduction and their applications in real-world datasets. DataCamp: "Dimensionality Reduction in R" - This interactive course teaches dimensionality reduction techniques in R, including random projection, and how to apply them to large-scale datasets for data analysis and visualization. Stanford Online: "Machine Learning" by Stanford University - This course covers various machine learning algorithms and techniques, including dimensionality reduction methods such as random projection, with practical examples and applications.
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