The age of big data is now coming. But the traditional data analytics may not be able to
handle such large quantities of data. The question that arises now is, how to develop a high …

The CUDA programming model provides a straightforward means of describing inherently
parallel computations, and NVIDIA's Tesla GPU architecture delivers high computational …

Compute, data, and algorithmic advances are the three fundamental factors that drive
progress in modern Machine Learning (ML). In this paper we study trends in the most readily …

The goal of this paper is to ascertain with what accuracy the direction of Bitcoin price in USD
can be predicted. The price data is sourced from the Bitcoin Price Index. The task is …

Support Vector Machines (SVMs) are classic supervised learning models for classification,
regression and distribution estimation. A survey conducted by Kaggle in 2017 shows that …

We introduce a new family of positive-definite kernel functions that mimic the computation in
large, multilayer neural nets. These kernel functions can be used in shallow architectures …

The promise of unsupervised learning methods lies in their potential to use vast amounts of
unlabeled data to learn complex, highly nonlinear models with millions of free parameters …

This chapter covers details of the support vector machine (SVM) technique, a sparse kernel
decision machine that avoids computing posterior probabilities when building its learning …

Kernel methods provide an elegant and principled approach to nonparametric learning, but
so far could hardly be used in large scale problems, since naïve implementations scale …

Convolutional neural networks (CNN) applications range from recognition and reasoning
(such as handwriting recognition, facial expression recognition and video surveillance) to …