A model might fail but still produce some output. We might not even get a signal of why or where our model fails. And there are many reasons for a model to fail. A model can fail due to bad data…
I’m writing a book on Retrieval Augmented Generation (RAG) for Wiley Publishing, and vector databases are an inescapable part of building a performant RAG system. I selected Qdrant as the vector…
Interconnected graphical data is all around us, ranging from molecular structures to social networks and design structures of cities. Graph Neural Networks (GNNs) are emerging as a powerful method of…
Traditional correlation coefficients such as Pearson ρ, Spearman, or Kendall’s τ are limited to finding linear or monotonic relationships and struggle to identify more complex association structures…
Discover how we applied Bayesian optimization to tune Word2Vec hyperparameters for a large-scale music recommendation system, boosting hit rate and NDCG metrics
Nvidia’s stock price has skyrocketed because of its GPU’s dominance in the AI hardware market. However, at the same time, TPUs, well-known AI hardware from Google, are not for sale. You can only rent…
Transformer architectures have been the dominant paradigm in LLMs leading to exceptional advancements in research and development. The question of whether transformers will be the final architecture…
Principal components analysis is a variance decomposition technique that is frequently used for dimensionality reduction. A thorough guide to PCA is available here. In essence, each principal…
I’ve always loved a dark background on a chart with neon lines for their aesthetic, and also their improved accessibility for certain types of vision impairments — in this article we’ll be discussing…
Frameworks like LangChain have definitely streamlined development, but hundreds of lines of code can still be a hurdle for those who aren’t programmers. That’s when I discovered “Lang Flow,” an…
