Knowledge graphs are network graphs that link related concepts and properties together to create a form of inferencing engine, with knowledge engineering being the programming aspect of graph usage. Explore how knowledge graphs are created and queried, how they are used as part of a broader form of enterprise metadata management, and how they tie into ML and the IoT.
This article was written by James Le. Neural networks are one type of model for machine learning; they have been around for at least 50… Read More »The 10 Deep Learning Methods AI Practitioners Need to Apply
Machine learning (ML) is a hot topic nowadays. Everyone speaks about the new programming paradigm, models are implemented in very different domains, more and more… Read More »Machine Learning Mindmap
Demand is the key indicator for every business to consider before taking the first step or expanding in the chosen market segment. It drives economic… Read More »The Complete Guide on Customer Demand Forecasting in Retail
Presentation on decision tree fundamentals such as finding best split, gini, entropy, misclassification error, gain ratio, numerical examples. Full presentation at Thank you Dr. Siddhaling… Read More »Decision Tree Fundamentals
Models are simplification or approximation of reality and hence they will not capture all of reality. “All models are wrong, but some are useful” is… Read More »Does "All models are wrong, but some are useful" quote apply to Machine learning models?
In the previous post, ten strategies to implement ai on the cloud and edge, I discussed strategies for end to end deployment for machine learning… Read More »Deploying machine learning models using Agile
Nowadays, artificial intelligence is present in almost every part of our lives. Smartphones, social media feeds, recommendation engines, online ad networks, and navigation tools are… Read More »Connections between Neural Networks and Pure Mathematics
Picture by By Tatiana Shepeleva/shutterstock.com One of the most challenging problems in modern theoretical physics is the so-called many-body problem. Typical many-body systems are composed… Read More »Neural Quantum States
For decision making, human perception tends to arrange probabilities into above 50% and below – which is plausible. For most probabilistic models in contrast, this… Read More »Setting the Cutoff Criterion for Probabilistic Models
Bayesian inference is the re-allocation of credibilities over possibilities [Krutschke 2015]. This means that a bayesian statistician has an “a priori” opinion regarding the probabilities… Read More »Naive Bayes Classifier using Kernel Density Estimation (with example)
