spaCy is a popular and easy-to-use natural language processing library in Python. It provides current state-of-the-art accuracy and speed levels, and has an active open source community. However, since SpaCy is a relative new NLP library, and it’s not as widely adopted as NLTK.
Source: towardsdatascience.com
Federal transport minister, Alexander Dobrindt, presented a report to Germany’s cabinet seeking to establish guidelines for the future programming of ethical standards into automated driving software. The report, was prepared by an automated driving ethics commission comprised of scientists and legal experts and produced 20 guidelines to be used by the automotive industry when creating automated driving systems. Shortly after its introduction, Dobrindt announced that the cabinet ratified the guidelines, making Germany the first government in the world to put such measures in place.
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Researchers recently successfully trained neural networks to generate level maps for Doom that, they report in a paper published to the arXiv preprint server in April, “proved to be interesting” to play. The work was carried out by researchers from the Polytechnic University of Milan and used Generative Adversarial Networks, a recent innovation in the field of deep learning.
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I recently started reading “Deep Learning with R”, and I’ve been really impressed with the support that R has for digging into deep learning. One of the use cases presented in the book is predicting prices for homes in Boston, which is an interesting problem because homes can have such wide variations in values. This is a machine learning problem that is probably best suited for classical approaches, such as XGBoost, because the data set is structured rather than perceptual data.
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