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.
Introduction Almost every machine learning algorithm comes with a large number of settings that we, the machine learning researchers and practitioners, need to specify. These… Read More »Cross-validation and hyperparameter tuning
This article was written by Fjodor Van Veen. With new neural network architectures popping up every now and then, it’s hard to keep track of them all.… Read More »The Neural Network Zoo
How data management is done in Milvus First of all, some basic concepts of Milvus: Table: Table is a data set of vectors, with each… Read More »Managing Data in Massive-Scale Vector Search Engine
An error correcting code (ECC) is a way of controlling errors in data that is being transmitted over an unreliable or noisy communication channel. In… Read More »Information Theory (Turbo Codes) & Bayesian Networks
Introduction to SageMaker I’ve been working with AWS SageMaker for a while now and have enjoyed great success. Creating and tuning models, architecting pipelines to… Read More »Deploy Your First Serverless AWS ML Solution Fast
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
