Apache Ignite is a distributed in-memory cache, query and processing platform. Discover how to build your own Apache Ignite persistence with Scala.
Transfer learning consists in training a base network and reusing some or all of this knowledge in a related but different task.
Transfer Style allows to use the inner understanding of an already trained Convolutional Neural Network to transfer style from one picture to another.
Data augmentation is a basic technique to increase our dataset without new data. Although the technique can be applied in a variety of domains, it’s very commonly used in Computer Vision, and this will be the focus of the post.
This post will focus on a class of metaheuristics known as Swarm Intelligence. The most amazing feature of these algorithms is their ability to solve complex problems by a set of cooperative agents posing very simple intelligence.
This post aims to show how to build an on-premise Mesos architecture to handle a disaster scenario when an entire Data Center is not available, covering also some framework strategies for zero data loss.
Correlation is very often used within the initial exploratory stage when given a dataset, because of its ability to comb through pairs of variables and swiftly summarize whether they appear to be related or not.
This is the second (and last) part of the series dealing with the formal comparison of Machine Learning (ML) algorithms from a statistical point of view. In this post, we examine how statistical tests are applied to performance data of ML algorithms.
Apache Ignite is a distributed in-memory cache, query and processing platform for working with large-scale data sets in real-time (leaving aside, streaming processing, Spark integration, Machine learning grid, Ignite FileSystem, persistence, transactions…)
Have you ever watched the cooking teaching shows? You have probably noticed that chefs have usually already all the ingredients separated and chopped. Likewise, a data scientist will be more useful and creative building models rather than spending time with data preprocessing…