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In machine learning, linear combinations of losses are all over the place. In fact, they are commonly used as the standard approach, despite that they are a perilous area full of dicey pitfalls. Especially regarding how these linear combinations make your algorithm hard to tune.
This article will focus on deploying our model including building a Chrome extension that can make calls to a REST API. Afterwards we will discuss how to setup continuous integration so that we can constantly update, test, and deploy the latest version of our project.
Because of the strong overreliance on p values in the scientific literature, some researchers have argued that we need to move beyond p values and embrace practical alternatives. When proposing alternatives to p values statisticians often commit the "statistician's fallacy," whereby they declare which statistic researchers really "want to know."
Data labelling is often the biggest bottleneck in machine learning — finding, managing and labelling vast quantities of data to build a sufficiently performing model can take weeks or months. Active learning lets you train machine learning models with much less labelled data. The best AI-driven companies, like Tesla, already use active learning. We think you should too.
This package provides easy to use, state-of-the-art machine translation for more than 100+ languages.