Deep learning (DL) has proven to be a highly effective approach for developing models in
diverse contexts, including visual perception, speech recognition, and machine translation …

Deep learning (DL) techniques have obtained remarkable achievements on various tasks,
such as image recognition, object detection, and language modeling. However, building a …

Modern approach to artificial intelligence (AI) aims to design algorithms that learn directly
from data. This approach has achieved impressive results and has contributed significantly …

Over the last decade, the long-running endeavour to automate high-level processes in
machine learning (ML) has risen to mainstream prominence, stimulated by advances in …

Abstract Machine Learning (ML) is integrated into a growing number of systems for various
applications. Because the performance of an ML model is highly dependent on the quality of …

Data pre-processing is one of the most time consuming and relevant steps in a data analysis
process (eg, classification task). A given data pre-processing operator can have positive …

Dataset scaling, aka normalization, is an essential preprocessing step in a machine learning
(ML) pipeline. It aims to adjust the scale of attributes in a way that they all vary within the …

Two factors are crucial for the effective operation of modern-day smart services: Initially, IoT-
enabled technologies have to capture and combine huge amounts of data on data subjects …

Automated Machine Learn (AutoML) process is target of large studies, both from academia
and industry. AutoML reduces the demand for data scientists and makes specialists in …

There is a clear correlation between data availability and data analytics, and hence with the
increase of data availability---unavoidable according to Moore's law, the need for data …