Processes deployed inside and across an enterprise, when executed can leave an operational data footprint in the form of an event logs, which if analyzed can be a valuable source of insights to support the management and improvement of business
Competing in a Data-Driven World
Most of the articles on data analytics would have convinced you by now that companies are capturing only a fraction of the potential value from the available data and that many organizations face challenges in incorporating data-driven insights into day-to-day
Latent Semantic Indexing with Apache Spark ML
In Information retrieval our starting point is often a term-document matrix constructed by converting the raw text of the corpus. Each row represents a term that occurs in the corpus(and values are its weight) and each column represents a
Maze Solver agent in Prolog
Prolog was invented in the early seventies by Alain Colmerauer and others at the University of Marseille. Prolog stands for Programmation en Logique (Programming in Logic). Prolog differs from the most common programming languages because it is a declarative langauge.
Solving Traveling Salesmen with Genetic Algorithms
The traveling salesman problem (TSP) is a typical example of a very hard combinatorial optimization problem. The problem is to find the shortest tour that passes through each vertex in a given graph exactly once. We will be use the
The Evolution of Data Products
"The purpose of (scientific) computing is insight, not numbers". - Richard Hamming A data product is a software application or tool that leverages data to deliver actionable insights, predictions, or automation, typically through the use of analytics, machine
Stateful dataflow graphs in tensorflow
TensorFlow is google’s second-generation system for the implementation and deployment of large-scale machine learning models. It is flexible enough to be used both in research and production. Computations in TensorFlow are expressed as stateful dataflow graphs. Essential Vocabulary: Basic
History of Deep Learning
Deep Neural Networks (DNNs) are neural networks with architecture consisting of multiple layers of perceptrons, designed to solve complex learning problems. Deep Neural Networks focus on the need to process and classify complex, high-dimensional data, requiring the use of a