Welcome back to our series on Swarm Intelligence Metaheuristics for Optimization. On this post, we will focus on Particle Swarm Optimization. Recall we define Metaheuristics as a class of optimization algorithms which turn out to be very useful when the function being optimized is non-differentiable or does not have an analytical expression at all.
There is one striking element that does not seem to have been addressed as a common purpose between business teams and IT teams when confronting Innovation or Digital Transformation roadmaps… A Data Management Strategy.
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.
Data analysts are often confronted with a seemingly difficult decision: to choose between a simple model or a more complex one. Discover more in this post in which Carlos del Cacho explains the unexplainable.
With this article, we want to start a series of posts on how to use Stratio Data Centric to solve problems that involve the analysis of large graphs.
