data science · machine learning

Deep Learning – NLP

https://zhuanlan.zhihu.com/p/49271699 Home https://jalammar.github.io/ Neural Language Model predict the next work, replace HMM, RNN LSTM different architectures stateful LSTM: memorize last batch. dependent stateless LSTM: update parameter in batch one, when batch two, initialize hidden states and cell states to zero. batch to batch. independent in different batches Word2Vec: CBOW, skip-grams | Glove (cannot solve the… Continue reading Deep Learning – NLP

data science · machine learning

Interpretability of ML

https://github.com/jphall663/awesome-machine-learning-interpretability https://christophm.github.io/interpretable-ml-book/index.html Global Interpretability Partial Dependence and Partial Dependence Plot (PDP) Individual Conditional Expectation (ICE) Total and two-way H Statistics Global Feature importance using permutation Global surrogate model Local Interpretability Local Interpretable Model-agnostic Explanations (LIME) Shapley additive explanation An intuitive way to understand the Shapley value is the following illustration: The feature values enter a… Continue reading Interpretability of ML

Big Data · Data Engineering · industry

Data in Real Scenarios

Log Management: logs are events with timestamps. Multi-cloud for regulatory reasons, and can handle the fail over of a single cloud. Elastic Search: full text searching, distributed? implicit index URBN: fashion product attribution. planning & forecasting; search correlation (Fashion MNIST data set) Linkedin: scaled of machine learning using graph database (neo4j), second degree. predicting future… Continue reading Data in Real Scenarios