The Levels of RAG (on Xebia.com ⧉)
LLM’s can be supercharged using a technique called RAG, allowing us to overcome dealbreaker problems like hallucinations or no access to internal data. But what can we expect from RAG? What use cases work well and which are more challenging? Let’s find out together!
RAG on GCP: production-ready GenAI on Google Cloud Platform (on Xebia.com ⧉)
GCP has an increasing set of managed services that can help you build production-ready Retrieval Augmented Generation (RAG) applications. How can you best leverage them to build RAG applications? Let’s explore together. Read along!
Dataset enrichment using LLM’s ✨ (on Xebia.com ⧉)
Large Language Models (LLM's) have proven to be useful for numerous tasks. LLM's can summarise text, generate text, classify text or translate text. What LLM's can also be used for is converting unstructured data to structured data.
From Linear Regression to Neural Networks
How are linear regression, logistic regression and neural networks related? What is overfitting and how do we fight it? In this post, we find answers to these questions in an interactive way by working with a real-world dataset on penguins.
Making Art with Generative Adversarial Networks
Can computers make art? To find out, we tried ourselves. We used Generative Adversarial Networks to try to paint new Van Gogh paintings.
Backdoors in Neural Networks
In this project, we demonstrated how Neural Networks can be vulnerable to a Backdoor attack.
COVID-19 Dashboard
What parts of the world are susceptible to Corona outbreak? We used Big Data and Data Engineering in this project to find out.
