Actualités des médias et des nouvelles technologies We and our partners process data for the following purposesActively scan device characteristics for identification, Audience Measurement, Create profiles for personalised advertising, Create profiles to personalise content, Develop and improve services, Measure advertising performance, Measure content performance, Store and/or access information on a device, Understand audiences through statistics or combinations of data from different sources, Use limited data to select advertising, Use limited data to select content, Use precise geolocation data, Use profiles to select personalised advertising, Use profiles to select personalised content
RSS Newsfeeds -- ScienceDaily Paper.li BigData Hebdo - BigData Hebdo Intelligence artificielle L'Usine Digitale respects your choices! We and our partners process data for the following purposesAnonymous audience statistics, Audience Measurement, Ensure security, prevent fraud, and debug, Functional cookies, Instant Messaging Chat, Personalised advertising and content, advertising and content measurement, audience research and services development , Precise geolocation data, and identification through device scanning, Store and/or access information on a device Machine Learning - KDnuggets 5 Free Courses to Understand Machine Learning Algorithms - Oct 24, 2024. To help you navigate this complex subject, we’ve compiled five free online courses that will give you a solid foundation in machine learning algorithms. Machine Learning 10 GitHub Repositories for Advanced Machine Learning Projects - Oct 16, 2024.Where can you find projects dealing with advanced ML topics? GitHub is a perfect source with its many repositories. I’ve selected ten to talk about in this article. Machine Learning Step-by-Step Guide to Deploying ML Models with Docker - Oct 8, 2024.Tired of fixing the same deployment issues?
towardsdatascience There are thousands of different tutorials out there that tell you how to explore your data. Most of them, however, focus on continuous data. Therefore, I won't waste any of your time (or mine) and I will stick to highlighting methods and tools that are specifically useful in survey data. Describe (Numpy version) There are a few inbuilt functions that can help you understand your a lot more, really fast. Describe is a really common tool that is used often by data scientists but this only accounts for the numeric and continuous variables. Some survey software's will output the questions already in one hot format. Groupby Crosstabs and Heatmaps Looking at subgroups of the data can be extremely important, especially in survey data. Groupby I won't dig too deep into how groupby works, but if you want to know more there’s a detailed explanation here. So with our data, we could produce something like this. Crosstabs We can do this with both raw counts or percentages as the code shows. Heat-maps
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What’s the difference between CPUs, GPUs and TPUs? Back at I/O in May, we announced Trillium, the sixth generation of our very own custom-designed chip known as the Tensor Processing Unit, or TPU — and today, we announced that it’s now available to Google Cloud Customers in preview. TPUs are what power the AI that makes your Google devices and apps as helpful as possible, and Trillium is the most powerful and sustainable TPU yet. But what exactly is a TPU? And what makes Trillium "custom"? To really understand what makes Trillium so special, it's important to learn not only about TPUs, but also other types of compute processors — CPUs and GPUs — as well as what makes them different. As a product manager who works on AI infrastructure at Google Cloud, Chelsie Czop knows exactly how to break it all down. Let’s start with the basics! These are all chips that work as processors for compute tasks. What do the different acronyms stand for? Even though CPUs, GPUs and TPUs are all processors, they're progressively more specialized.
Which Data Science Skills are core and which are hot/emerging ones? - KDnuggets The latest KDnuggets Poll asked 1. Which skills / knowledge areas do you currently have (at the level you can use in work or research)? We selected a list of 30 skills based on a number of previous KDnuggets articles and polls - see useful links at the end of this post, as well as external sources. Altogether(*), this poll received over 1,500 votes - a large enough sample to make meaningful inferences. Fig. 1 below shows key findings, with X-axis showing % Have Skill - answers to the first poll question, and Y-axis showing % Want Skill - answers to the 2nd poll question. Fig. 1: Data Science-related Skills, Have skill vs Want to add or improve skill We note two main clusters in this chart. Cluster 1, in blue dashed rectangle on the right side of the chart, includes skills that over 40% of all voters have, and where the ratio of Want/Have is less than 1. Table 1: Core Data Science Skills, in decreasing order of %Have Table 3: Other Data Science Skills, in decreasing order of %Have Related: