Machine Learning

An exclusive sneak-peek at a revolutionary new method for training neural networks — My good friend and humble genius Dr Danko Nikolić recently shared an unpublished paper with me, thinking I might be interested. Was I ever. Reading it made me feel like I was witnessing a historic moment before anyone else did, and I was immediately bursting to share. Thankfully, Danko agreed…

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a clustering algorithm that groups together points that are close to each other based on a density criterion. In contrast with other Clustering models such as K-means, DBSCAN can identify clusters of arbitrary shapes and does not require the number of…

Effortless data deduplication at scale. — In today’s data-driven world, the importance of high-quality data to build quality systems cannot be overstated. The availability of reliable data is highly critical for teams to make informed decisions, develop effective strategies, and gain valuable insights. However, at times, the quality of this data gets compromised by various factors…

In this blog, you will discover why machine learning practitioners should study linear algebra to improve their skills and capabilities as practitioners. After reading this blog, you will understand how can linear algebra be applied in machine learning. WHAT IS LINEAR ALGEBRA? Linear algebra is the study of vector spaces, lines and planes, and…

It has been helpful in automation, improved decision making, personalisation and innovation (including that for self-driving cars, language processing, healthcare diagnostics and speech/facial recognition). As advanced AI systems like ChatGPT, DeepMind Sparrow, and others continue to develop, certainly, AI will eventually become an integral part of our everyday lives. AI…