… In terms of advancing machine learning as an academic discipline, this approach has thus …
problems in machine learning are those that arise during its application to real-world problems…

… Thus, we discuss opportunities and challenges of ML on big data from the following
perspectives: distributed ML, parallelization in several primary ML paradigms, and deep learning. …

… in every problem, requiring novel research in machine learning. This … in the machine learning
(ML) community to these challenges offered by geoscience problems and the opportunities …

… Deep learning is working on both classification and … machine learning and training and
testing data are similar to traditional machine learning, except traditional machine learning …

… some common types of machine learning models. Finally, we discuss some opportunities
and challenges for the materials community to fully utilize the capabilities of machine learning. …

… Within these fields, there are multiple unique challenges that exist. … challenges, as well as
create further research opportunities. Accordingly, this work surveys some of the challenges …

… on the industrial implementation issues of ML projects, particularly for machine vision (MV)
tasks. … to realistically evaluate the opportunities and challenges involved in implementing ML …

… machine learning deployment workflow. By mapping found challenges to the steps of the
machine learning deployment workflow, we show that practitioners face issues at each stage of …

… and organizes machine learning challenges with Big Data. In contrast to other research
that … challenges, this work highlights the cause-effect relationship by organizing challenges …

… machine learning in the medical space have focused narrowly on biomedical applications 5
, deep learning tasks … the broad opportunities present in machine learning for healthcare and …