고객 생애주기에 따른 유저 세그멘트와 CRM 마케팅 https://brunch.co.kr/@yi-seo/103 고객 생애주기에 따른 유저 세그먼트와 CRM마케팅 예상고객, 신규고객, 충성고객, 휴면고객, 복귀고객의 Life Cycle | 한 사람을 만나 설레는 감정을 느끼고 예상치 못한 이별을 하거나 다시 만남을 통해 사랑에 빠지는 일련의 과정을 우리는 인생이 brunch.co.kr Predictive CLV란 무엇이고 왜 중요한 건가요? https://blog.dighty.com/expert/?bmode=view&idx=11226052&t=board&src=image&kw=00002E 다이티가 알려주는 CLV에 대한 거의 모든 것 (실전편) : 다이티 블로그 - Expert 이전 콘텐츠에서 많은 비즈니..

“Understanding Singular Value Decomposition and its Application in Data Science” by Reza Bagheri https://link.medium.com/doiKvuUOf3

https://arxiv.org/abs/1812.11118?fbclid=IwAR1zZ2RuTEIeEFPiFGvfMiyt-esJlmQOfUe2-mHK5l0mkf-pQiLBlEINtdc Reconciling modern machine learning practice and the bias-variance trade-off Breakthroughs in machine learning are rapidly changing science and society, yet our fundamental understanding of this technology has lagged far behind. Indeed, one of the central tenets of the field, the bias-variance t..

Neural Network에 ensemble 개념을 넣은 것이 구현된 것이 있는 medium tensorflow2.0으로 되어있지만 1.x로 바꿔볼 예정 성능이 기대가 됨! https://medium.com/b-telligent-data-science-ai/neural-averaging-ensembles-for-tabular-data-with-tensorflow-2-0-f28796e1289f Neural averaging ensembles for tabular data with TensorFlow 2.0 Neural networks for tabular data: ensemble learning without trees medium.com https://nbviewer.jupyter.org/github/..

https://levelup.gitconnected.com/sort-elements-in-python-817a0c2b810b Sort Elements in Python Containers are objects that contain other objects. List, tuples, or sets are examples of built-in containers in Python. These containers… levelup.gitconnected.com

https://towardsdatascience.com/stacking-classifiers-for-higher-predictive-performance-566f963e4840 Stacking Classifiers for Higher Predictive Performance Using the Wisdom of the Multiple Classifiers to Boost Performance towardsdatascience.com

너무 길어서 다 못 읽었지만 다음에 꼭 읽기 위해서 블로그에 올린다 “Understanding Differential Privacy” by An Nguyen https://link.medium.com/t4gMaLX7XX

https://towardsdatascience.com/why-using-a-mean-for-missing-data-is-a-bad-idea-alternative-imputation-algorithms-837c731c1008 Why using a mean for missing data is a bad idea. Alternative imputation algorithms. We all know the pain when the dataset we want to use for Machine Learning contains missing data. The quick and easy workaround is to… towardsdatascience.com 가장 인상 깊은 부분은 이것 Mean reduces a ..

https://towardsdatascience.com/7-tips-for-dealing-with-small-data-7ffbd3d399a3 7 Tips for Dealing With Small Data Because more often than not, that’s what you’re gonna get. towardsdatascience.com 자체 글도 좋지만, 주변에 참고하는 URL들이 다 주옥 같은 것 같다! Improving the Realism of Synthetic Images https://machinelearning.apple.com/2017/07/07/GAN.html Improving the Realism of Synthetic Images - Apple Apple Machine Le..

https://towardsdatascience.com/deep-dive-into-catboost-functionalities-for-model-interpretation-7cdef669aeed Deep dive into Catboost functionalities for model interpretation Do we really understand what happens inside ML models we build? Let’s explore. towardsdatascience.com 일단 나중에라도 꼭 볼 것 Catboost를 알고리즘과 해석하는 라이브러리를 결합하여서 설명함. Code도 있어서 유용함! https://github.com/catboost/tutorials/blob/master/mod..

https://bitbucket.org/ispamm/group-lasso-for-neural-networks-tensorflow-keras/src/master/ Bitbucket bitbucket.org https://towardsdatascience.com/regularization-for-neural-networks-with-framingham-case-study-2c51cca72f7c Regularization for Neural Networks with Framingham Case Study L1, L2, elastic net, and group lasso regularization towardsdatascience.com

“Dealing with the Lack of Data in Machine Learning” by Alexandre Gonfalonieri https://link.medium.com/YW2pTB09LW “Dealing with the Lack of Data in Machine Learning” A place where words matter link.medium.com