This paper reviews some of the challenges posed by the huge growth of experimental data
generated by the new generation of large-scale experiments at UK national facilities at the …

Deep learning has transformed the use of machine learning technologies for the analysis of
large experimental datasets. In science, such datasets are typically generated by large-scale …

The current interest in big data, machine learning and data analytics has generated the
widespread impression that such methods are capable of solving most problems without the …

Many scientific communities have expressed a growing interest in machine learning
algorithms recently, mainly due to the generally good results they provide, compared to …

Recent advances in machine learning methods, along with successful applications across a
wide variety of fields such as planetary science and bioinformatics, promise powerful new …

Traditional books on machine learning can be divided into two groups-those aimed at
advanced undergraduates or early postgraduates with reasonable mathematical knowledge …

You must understand algorithms to get good at machine learning. The problem is that they
are only ever explained using Math. No longer. In this Ebook, finally cut through the math …

Scientific Machine Learning (SciML) and Artificial Intelligence (AI) will have broad use and
transformative effects across the Department of Energy. Accordingly, the January 2018 Basic …

I outline how over my career as a protein scientist Machine Learning has impacted my area
of science and one of my pastimes, chess, where there are some interesting parallels. In …

Neural network-based machine learning is capable of approximating functions in very high
dimension with unprecedented efficiency and accuracy. This has opened up many exciting …