https://thegradient.pub/interpretability-in-ml-a-broad-overview/ Interpretability in Machine Learning: An Overview21.Nov.2020Owen Shen “Broadly, interpretability focuses on the how. It’s focused on getting some notion of an explanation for the decisions made by our models….
What I Read: Building Robust Machine Learning Systems
https://medium.com/swlh/deepminds-three-pillars-for-building-robust-machine-learning-systems-a9679e56250a DeepMind’s Three Pillars for Building Robust Machine Learning SystemsSpecification Testing, Robust Training and Formal Verification are three elements that the AI powerhouse believe hold the essence of robust machine
What I Read: HuggingFace Transformers
https://medium.com/georgian-impact-blog/how-to-incorporate-tabular-data-with-huggingface-transformers-b70ac45fcfb4 How to Incorporate Tabular Data with HuggingFace TransformersGeorgianOct 23 “At Georgian, we find ourselves working with supporting tabular feature information as well as unstructured text data. We found that
What I Read: Isolation Forest
https://towardsdatascience.com/isolation-forest-is-the-best-anomaly-detection-algorithm-for-big-data-right-now-e1a18ec0f94f Isolation Forest is the best Anomaly Detection Algorithm for Big Data Right NowAndrew YoungNov 13 “Isolation forest or “iForest” is an astoundingly beautiful and elegantly simple algorithm that identifies