In literature, traditional classifiers are used in classification studies done on social network sites and semantic relations of words are ignored. They ignore the natural behaviors of natural languages like synonym, polysemy, multi-word expressions and latent-semantics.
Since its introduction with Nakamoto’s famous Bitcoin paper, the concept of blockchain has been gaining attraction all over the world. While development of new technologies is becoming more of an issue, teaching how to use them in an effective way provides new opportunities and challenges. Blockchain technologies has their own understanding in the manner of learning and teaching. Therefore teaching blockchain concept and programming requires innovative approach in order to reach better learning experience.
Machine learning algorithms are widely used in engineering and business.These algorithms scale and speed varies over different applications. Most of them relies on vast amount of data in limited amount of time and space. To handle these vast data we need distributed versions of the algorithms we have. In this work parallelization of machine learning algorithms were main problem focused on. The problem tried to parallelize is matrix factorization. We used stochastic gradient descent approach to minimize a linear loss function such as RMSE.
