[Intuitive Deep Learning] 1.6 Generalizaed matrix factorization | Multiview data clustering using MF

#IntuitiveDeepLearning #SimpleMathematicsOfDL #LinearAlgebra Unlock the world of Deep Learning with our new “Intuitive Deep Learning” video series! 🚀 Dive into the mathematical foundations of DL using an intuitive approach and the power of linear algebra. 🧠 We hope that you will find the joy of clarity and multi-layer understanding of DL methods in this new series. In this video we briefly cover the following topics: 1. Generalized matrix factorization Paper link: https://www1.cmc.edu/pages/faculty/bhunter/papers/deepNMF.pdf Reference: Flenner, Jennifer, and Blake Hunter. "A deep non-negative matrix factorization neural network." Semantic Scholar (2017). 2. Multi-view data clustering using deep matrix factorization Paper link: https://ojs.aaai.org/index.php/AAAI/article/view/10867 Reference: Zhao, Handong, Zhengming Ding, and Yun Fu. "Multi-view clustering via deep matrix factorization." Proceedings of the AAAI conference on artificial intelligence. Vol. 31. No. 1. 2017. 📚🔗 The summary map of the DL linear algebra of the first lecture videos 1.1 to 1.6 can be found at: https://drive.google.com/file/d/198s8Tr8AGHplQaRZXcLawshiakGTxXKA/view?usp=sharing 📚🔗 Access the lecture material here: https://drive.google.com/file/d/1PDdH_pa9jbpKd3LpzPy_KuOKHSV0tygY/view?usp=sharing 📊💡 Join us on this journey to gain a simplified understanding of complex concepts in DL. Special thanks go to Jeremey Watts, Simon Prince and co-authors for producing foundational textbooks: • Machine Learning Refined; https://github.com/jermwatt/machine_learning_refined • Understanding Deep Learning; https://github.com/udlbook/