A portfolio is one way to show people you are that data science unicorn.

Dean Eckles writes:

Dean Eckles writes:

Statistical Methods for Life Data Analysis (weibulltools)Contains methods for examining bench test or field data using the well-known Weibull Analysis. It includes Monte Carlo simulation for estimating the li …

Dual Supervised Learning Many supervised learning tasks are emerged in dual forms, e.g., English-to-French translation vs. French-to-English translation, speech recognition vs. text to speech, and image classification vs. image generation. Two dual tasks have intrinsic connections with each other due to the probabilistic correlation between their models. This connection is, however, not effectively utilized today, since people usually train the models of two dual tasks separately and independently. In this work, we propose training the models of two dual tasks simultaneously, and explicitly exploiting the probabilistic correlation between them to regularize the training process. For ease of reference, we call the proposed approach \emph{dual supervised learning}. We demonstrate that dual supervised learning can improve the practical performances of both tasks, for various applications including machine translation, image processing, and sentiment analysis. …

We illustrate the application of two linear compression algorithms in python: Principal component analysis (PCA) and least-squares feature selection. Both can be used to compress a passed array, and they both work by stripping out redundant columns from the array. The two differ in that PCA operates in a particular rotated frame, while the feature selection solution operates directly on the original columns. As we illustrate below, PCA always gives a stronger compression. However, the feature selection solution is often comparably strong, and its output has the benefit of being relatively easy to interpret — a virtue that is important for many applications.

I left Facebook and Twitter more than 2 years go (with excellent results, I have to say), and due to being a bit tired, I stopped publishing the Data Links series. I miss a bit being able to share quickly those articles that I think are worth it, in this age of information overload, saturation, fake news, fake fake news, so-called news and so on.

“At one time we had wisdom, but little knowledge. Now we have a great deal of knowledge, but do we have enough wisdom to deal with that knowledge? I define wisdom as the capacity to make retrospective judgments prospectively. I think these are human qualities, human attributes that need to be brought out, need to be drawn upon, need to be valued.” Jonas Salk ( 1991 )

A couple people pointed me to this article, “How to Beat Science and Influence People: Policy Makers and Propaganda in Epistemic Networks,” by James Weatherall, Cailin O’Connor, and Justin Bruner, also featured in this news article. Their paper begins:

Current machine learning systems operate, almost exclusively, in a statistical, or model-free mode, which entails severe theoretical limits on their power and performance. Such systems cannot reason about interventions and retrospection and, therefore, cannot serve as the basis for strong AI. To achieve human level intelligence, learning machines need the guidance of a model of reality, similar to the ones used in causal inference tasks. To demonstrate the essential role of such models, I will present a summary of seven tasks which are beyond reach of current machine learning systems and which have been accomplished using the tools of causal modeling. Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution