  McKernel introduces a framework to use kernel approximates in the mini-batch
setting with Stochastic Gradient Descent (SGD) as an alternative to Deep
Learning. Based on Random Kitchen Sinks [Rahimi and Recht 2007], we provide a
C++ library for Large-scale Machine Learning. It contains a CPU optimized
implementation of the algorithm in [Le et al. 2013], that allows the
computation of approximated kernel expansions in log-linear time. The algorithm
requires to compute the product of matrices Walsh Hadamard. A cache friendly
Fast Walsh Hadamard that achieves compelling speed and outperforms current
state-of-the-art methods has been developed. McKernel establishes the
foundation of a new architecture of learning that allows to obtain large-scale
non-linear classification combining lightning kernel expansions and a linear
classifier. It travails in the mini-batch setting working analogously to Neural
Networks. We show the validity of our method through extensive experiments on
MNIST and FASHION MNIST [Xiao et al. 2017].
