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.
That’s correct. 1 server and 10 lines of code are all you need to search among 1 billion images in a few hundred milliseconds. Its… Read More »The Easiest Way to Search Among 1 Billion Image Vectors And Why Vector Search Is Important?
Brian Huge and I just posted a working paper following six months of research and development on function approximation by artificial intelligence (AI) in Danske Bank.… Read More »Differential ML on TensorFlow and Colab
The challenge with data search The explosion in unstructured data, such as images, videos, sound records, and text, requires an effective solution for computer vision,… Read More »Milvus: A big leap to scalable AI search engine
In supervised machine learning algorithms, Random Forest stands apart as it is arguably the most powerful classification model. When Microsoft developed their X-box game which… Read More »Random Forest Classification explained in detail and developed in R
In my previous post, I introduced the ELAINE Community Tool that can be used to discover variables from textual communications. Statistical predictors work well for… Read More »ELAINE use case – Improving statistical prediction on financial market with Symbolic Logic
This article was written by Graph Commons. A common task for a data scientist is to identify clusters in a given data set. The idea… Read More »Finding organic clusters in complex data-networks
Today, Google published the following paper: TensorFlow Quantum: A Software Framework for Quantum Machine Learning, TensorFlow Quantum is software for doing Quantum Bayesian Networks. QB… Read More »Google Releases TensorFlow Quantum
Machine Learning (ML) models are increasingly being used to augment human decision making process in domains such as finance, telecommunication, healthcare, and others. In most… Read More »How you can explain Machine Learning models ?
Knowledge acquisition is about building generalization capabilities. In machine learning world, generalization refer to the model’s ability to make accurate predictions from never before seen… Read More »Ability to generalize – A measure of intelligence ?
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
