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Software commodities are eating interesting data science work

Data Science and Beyond

My story seems to reflect that: From my first steps in sentiment analysis and topic modelling, through building recommender systems while dabbling in Kaggle competitions and deep learning a few years ago, and to my present-day interest in causal inference. I learned about Bayesian statistics and conjugate priors.

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Adding Common Sense to Machine Learning with TensorFlow Lattice

The Unofficial Google Data Science Blog

On the one hand, basic statistical models (e.g. On the other hand, sophisticated machine learning models are flexible in their form but not easy to control. Monotonic Deep Lattice Networks Deep learning is a powerful tool when we have an abundance of data to learn from.

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Credit Card Fraud Detection using XGBoost, SMOTE, and threshold moving

Domino Data Lab

In contrast, the decision tree classifies observations based on attribute splits learned from the statistical properties of the training data. Machine Learning-based detection – using statistical learning is another approach that is gaining popularity, mostly because it is less laborious. describe().

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Themes and Conferences per Pacoid, Episode 9

Domino Data Lab

For example, in the case of more recent deep learning work, a complete explanation might be possible: it might also entail an incomprehensible number of parameters. They also require advanced skills in statistics, experimental design, causal inference, and so on – more than most data science teams will have.

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Data Science at The New York Times

Domino Data Lab

When he retired in 2009 he had some time on his hands. ” And this is one of his papers about “you’re doing it wrong” where he talked about the algorithmic culture that he was observing in the machine learning community versus the generative model community that was more traditional in statistics.