With recent breakthroughs in Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP), financial companies are beginning to pass the task of identifying and executing trades on to automated systems. Algorithmic Trading (AT), smart stock advisors, and ‘self-learning’ Reinforcement Learning (RL) agents now powerful tools in the financial decision-making process....
Articles Published by Kirill Goltsman
The paper describes the architecture of a simple neural network and offers a useful intuition on how it may be used to solve complex nonlinear problems in an efficient way.
Data mining refers to discovery and extraction of patterns and knowledge from large data sets of structured and unstructured data. Data mining techniques have been around for many decades, however, recent advances in ML (Machine Learning), computer performance, and numerical computation have made data mining methods easier to implement on the large data sets and in business-centric tasks.
Contemporary ML (Machine Learning) research is powered by huge volumes of Big Data, such as user ratings, search queries, uploaded images, and product reviews. To be useful, this data must be labeled, deduplicated, cleaned up, and regularized.
To be effective, recommender systems should strike the right balance between personalized and unpersonalized features. Let's see how Netflix solves this problem with its multi-purpose recommender algorithms.