Avoiding false conclusions with the proper tooling — Data Analysis is — as indicated by that profession’s name — an integral part of a Data Scientist’s work, ranging from descriptive statistics and simple regression models to sophisticated machine learning approaches. However, those approaches have to be handled with care, and selecting the right approach is far away from…

And why prompting is none of them — In the realm of large language models (LLMs), there is a variety of training mechanisms with different means, requirements, and goals. As they serve different purposes, it is important to not confuse them with each other and to be aware of the different scenarios they are applicable to. In this…

Setting up an LLM to talk about your code — with LangChain and local hardware — Among the various tasks Large Language Models (LLMs) can perform today, code understanding may be of particular interest for you, if you work with source code as a software developer or a data scientist. Wouldn’t it be great to have a chatbot you can ask questions about your code? Where…

Enriching prompts to steer the model — explained for non-experts — Large language models like GPT show astonishing capabilities for solving complex tasks like translation, reasoning, or answering questions. However, to arrive there, the models have to be queried in the correct way; a technique called prompting. …

Breakthroughs on the way towards GPT — explained for non-experts — The overwhelming attention large language models like GPT get in media today creates the impression of an ongoing revolution we all are in the middle of. However, even a revolution builds on the successes of its predecessors, and GPT is the result of decades of research. In this post, I…

Other criteria you should consider during model selection — As a data scientist or machine learning engineer, you spend much of your time improving a model’s performance by creating new features, comparing different types of models, trying out new model architectures, and much more. In the end, it’s the score on the test set that counts, so that is…

Why causing your model to fail is the best thing you can do — When developing a new model or algorithm, it is tempting to test it over and over again with similar examples that all work perfectly. While this may be fun, it doesn’t really help you in understanding and improving your model. You learn from errors, so cause your model to fail! …