#machine learning

Watch Vadim Markovtsev's ML Conference session

Mining software development history: Approaches and challenges

This talk from the Machine Learning Conference gives a fun history of mining examples and presents some of the available tooling. Some of the topics we’ll be going over include embeddings, dynamic time warping, seriation, and HDBSCAN. Watch Vadim Markovtsev’s ML Conference session and come away knowing more about software development.

Personalizing city experiences

Artificial intelligence & machine learning: The brain of a smart city

In this article, explore how a combination of artificial intelligence and machine learning can act as the brains of a smart city while simultaneously considering how a smart city experience can become more personalized without compromising the privacy of its residents. Read on to see what the advantages and disadvantages of an ML and AI-powered smart city are.

Watch Christoph Windheuser's ML Conference session

How to implement chatbots in an industrial context

Chatbots are among the most popular applications of artificial intelligence, machine learning, and natural language processing, and many people are already familiar with them. Various companies are developing first prototypes to improve their customer communication and support functions. How do we begin to implement them into an industrial context?

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.