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#machine learning

360-degree view of your problems

Overview of ycrash – finding the source of your problem

Take a tour of ycrash in this article by Ram Lakshmanan. ycrash helps capture critical artifacts, including garbage collection logs, thread dumps, core dumps, heap dumps, disk usage, and more when the problem happens. It applies machine learning algorithms and generates a report which gives you a complete view of the problem, down to the lines of code that caused it.

Watch Juantomás Garcia Molina's ML Conference session

How we used reinforcement learning to solve the Abbey of Crime

Can AI play and complete a game? Juantomás Garcia Molina’s session from the Machine Learning Conference looks at developing artificial intelligence that can complete the first 3-D RPG, created in 1987. Many people had difficulty completing this technological wonder, so how will artificial intelligence fare?

Watch Fabian Hertwig's ML Conference session

Predicting New York City Taxi demand: Spatio-temporal time series forecasting

Machine learning can help predict things dependent on time such as taxi demand. Time series forecasting has always been an important field in machine learning and statistics, as it helps us to make decisions about the future. A special field is spatio-temporal forecasting, where predictions are not only made on the temporal dimension, but also on a regional dimension.

Watch Michael Kieweg's Machine Learning Conference session

The more data, the better the AI, isn’t it?

In this session, speaker Michael Kieweg will discuss data and AI and the relationship between the two. Get comfortable and watch his session from the Machine Learning conference where he discusses how to tackle challenges related to data quality and how to use data for better artificial intelligence performance.

Where the concept of sampling began

Are you stuck in the past? A case against data sampling Part I

Increasingly large and diverse data sets allow us to form complex insights. With all this data, why would we limit ourselves by using data sampling instead? Sampling only works when it is put in the hands of data science specialists. In this article, learn about some of the downsides of using data sampling and how it limits and undermines business decisions. Read part one of the case against data sampling.

Laying the groundwork for big data

Building a data platform on Google Cloud Platform

At the moment, big data is very popular and there is a wide variety of products available for handling data. In this article, read a case study about a German startup tackled their data problems and built a common data platform into their architecture. The data platform consists of four components: Ingestion, storage, process, and provisioning.

Data-powered solutions

How will AI impact the e-commerce industry?

Artificial intelligence and machine learning are changing many industries, including e-commerce. This article examines some of the biggest trends in e-commerce that have appeared thanks to the growing use of AI technologies. Using these technologies can set your enterprise ahead of the competition and give you the edge you need.

Watch Thiago da Silva Alves and Jean Metz's Machine Learning Conference 2019 session

Honey bee conservation using deep learning

Honey bee colony assessment is usually carried out via the laborious manual task of counting and classifying comb cells. Beekeepers perform this task many times throughout the year to asses the colony’s strength and to track its development. As you can imagine, this is an extremely time-consuming and error-prone task.