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Daily Links 08/11/2017

Machine Learning vs Statistics: The Texas Death Match of Data Science [Tomm Fawcett & Drew Hardin / Silicon Valley Data Science]

Since decisions still have to be made, statistics provides a framework for making betterdecisions. To do this, statisticians need to be able to assess the probabilities associated with various outcomes. And to do that, statisticians use models. In statistics, the goal of modeling is approximating and then understanding the data-generating process, with the goal of answering the question you actually care about.

In contrast to Statistics, note that the goal here to generate the best prediction. The ML practitioner usually does some exploratory data analysis, but only to prepare the data and to guide the choice of features and a model family. The model does not represent a belief about or a commitment to the data generation process. Its purpose is purely functional. No ML practitioner would be prepared to testify to the “validity” of a model; this has no meaning in Machine Learning, since the model is really only instrumental to its performance.2 The motto of Machine Learning may as well be: The proof of the model is in the test set.

I’m a woman in computer science. Let me ladysplain the Google memo to you. [Cynthia Lee / Vox]

To be a woman in tech is to know the thrill of participating in one of the most transformative revolutions humankind has known, to experience the crystalline satisfaction of finding an elegant solution to an algorithmic challenge, to want to throw the monitor out the window in frustration with a bug and, later, to do a happy dance in a chair while finally fixing it. To be a woman in tech is also to always and forever be faced with skepticism that I do and feel all those things authentically enough to truly belong. There is always a jury, and it’s always still out.

This Morning Routine Will Save You 20+ Hours a Week [Benjamin P. Hardy / Inc.]

The same concept applies to work. The best work happens in short intensive spurts. By short, I’m talking 1-3 hours. But this must be “Deep Work,” with no distractions, just like an intensive workout is non-stop. Interestingly, your best work – which for most people is thinking – will actually happen while you’re away from your work, “recovering.”

For best results: Spend 20% of your energy on your work and 80% of your energy on recovery and self-improvement. When you’re getting high quality recovery, you’re growing. When you’re continually honing your mental model, the quality and impact of your work continually increases. This is what psychologists call, “Deliberate Practice.” It’s not about doing more, but better training. It’s about being strategic and results-focused, not busyness-focused.

The Labyrinth of Life [Martha Beck]

Today, if you’re confronting an issue for the ten thousandth time, or feeling that your life is going nowhere, or panicking over how little you’ve achieved, stop and breathe. You’re not falling behind on some linear race through time. You’re walking the labyrinth of life. Yes, you’re meant to move forward, but almost never in a straight line. Yes, there’s an element of achievement, of beginning and ending, but those are minor compared to the element of being here now. In the moments you stop trying to conquer the labyrinth of life and simply inhabit it, you’ll realize it was designed to hold you safe as you explore what feels dangerous. You’ll see that you’re exactly where you’re meant to be, meandering along a crooked path that is meant to lead you not onward, but inward.

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