The last chapter of the three-part series on Evolutionary Feature Selection with Big datasets. We will address some fundamental design aspects of a Genetic Algorithm (GA) and commonly chosen options, to then move on to the CHC algorithm and a distributed approach for Feature Selection.
The key to the success of these methods lies in the rich representations these models build, which are generated after an exhaustive and computationally expensive learning process. Deep learning is starting to become essential to specialized areas of business. Nonetheless, it is not the panacea…
Data has become for many companies their most important asset. Ensuring quality of the information, as well as carrying out a proper management and governance of the data, is absolutely essential.
This is the second chapter of a three-part series on Evolutionary Feature Selection with Big datasets. We will start where we left off, namely with a review of existing metaheuristics with special focus on Genetic Algorithms.
Spark Streaming is one of the most widely used frameworks for real time processing in the world with Apache Flink, Apache Storm and Kafka Streams. However, when compared to the others, Spark Streaming has more performance problems and its process is through time windows instead of event by event, resulting in delay.
The human brain and our algorithms are hardly alike, as Neuroscience and Deep Learning are quite different disciplines, but some of the concepts still give support to some ideas. In this post, we will talk about one of those ideas: the memory.