John Paul Newport on Walter Isaacson, “It was always amazing … he could retreat up to the bedroom for a while, when the rest of us were chilling on the patio or whatever, to work on his book … he’d go up for twenty minutes or an hour, we’d hear the typewriter pounding, then...
Newport defines deep work as, “the professional activities performed in a state of distraction-free concentration that pushes your cognitive capabilities to their limit”. Basically, deep work is work that you do with all of your focus on the job at hand, resulting in greater productivity and ...
In his book Daily Rituals, j ournalist Mason Currey sorted through various sources on Jung to re-create the psychiatrist’s work habits at the Tower. Jung would rise at seven a.m., Currey reports, and after a big breakfast he would spend two hours of undistracted writing time in his pr...
creating a routine. While deep work won’t necessarily take up your entire day, you will protect space for it every day. It’s particularly handy for work that lacks pressure, like writing a book. Creating a rhythmic habit, where you do a bit of work every evening, can help you maintai...
Official website of Georgetown Professor Cal Newport, author of the Study Hacks blog, Deep Work, and other New York Times bestselling books.
However, you have to learn how to work deeply to make an impact on your workplace, placed in today’s information economy. And you can start by reading“Deep Work”and using the presented tools and steps. We recommend this book to everyone who has had enough of just working automatically...
It’s not until the third stage that the actual machine learning work begins. In this notebook, I’ll create a model to separate the benign growths from the malignant ones. Logistic Regression is a simple linear model that takes input values (represented by the blue circles in Figure 4) ...
the book deep work,by cal newport英语阅读 《深度工作》(Deep Work)是卡尔·纽波特(Cal Newport)所著的一本书。以下是关于这本书的一些信息: 内容简介:《深度工作》探讨了在当今充满分心和浅层次工作的环境中,专注和深度工作的重要性。作者认为,深度工作是提高生产力、创造力和个人满足感的关键。 主要观点:...
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DL needs a large amount of data, while ML can also work with a small amount of data; in DL, the feature engineering phase is eliminated; DL can learn features for improving the output accuracy. On the other side, in ML, the feature engineering phase is application dependent and manually ...