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.
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.