Introduction In September 2024, researchers demonstrated a startling result: fine-tuning GPT-3.5 Turbo on just 10 harmful examples was enough to break its safety alignment. The model would comply ...

Introduction Imagine spending $100M training a state-of-the-art model, only to have a competitor reconstruct it for $1,000 by simply querying your API. That’s not a thought experiment — it’s a pub...

Introduction On March 26, 2026, a routine pip install litellm gave thousands of ML teams something they didn’t expect — a credential stealer that harvested SSH keys, cloud provider secrets, and Ku...

Introduction In our previous posts, we built a Knowledge Graph with Neo4j and analyzed it using Graph Algorithms like PageRank. These methods are powerful, but they are often limited to “shallow” ...

Introduction In our previous tutorial, we built a Knowledge Graph of the African Tech Ecosystem. But a graph is more than just a storage mechanism—it’s a powerful structure for analysis. In this ...

Introduction In our previous post, we explored the theory behind Knowledge Graphs (KGs). Now, it’s time to get our hands dirty! In this tutorial, we will build a Knowledge Graph of the African Te...

Introduction In the age of Big Data, we have more information than ever, but connecting that information to derive meaning remains a challenge. Knowledge Graphs (KGs) offer a powerful solution by ...

Introduction While first-order methods like SGD and Adam dominate deep learning, second-order optimization methods offer powerful alternatives by exploiting curvature information from the loss lan...

Introduction While vanilla gradient descent laid the foundation for modern machine learning, training deep neural networks requires more sophisticated optimization techniques. In this comprehensiv...

What is Gradient Descent? Gradient descent is the workhorse optimization algorithm behind most machine learning models. It’s an iterative method for finding the minimum of a function by following ...