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!
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.