Decoding the AI jargon — Since the advent of ChatGPT in November 2022, Generative AI became one of the hottest topics to talk about. …

A brief introduction — In last weeks, many of us have been experimenting with the powerful Azure OpenAI APIs, either in the playground or via REST API or Python SDK. However, those APIs alone are not sufficient to build powerful applications. We can summarize in three main categories the kind of results we want…

An implementation with Python and Streamlit-Chat — ChatGPT was one of the most stunning developments of OpenAI. Since its release to the general public in November 2022, it has been used for so many use cases. However, we are talking about a web preview that cannot be customized both in the backend and front end. That is…

An implementation with Python — Imagine you have a lot of structured data and you want to extract relevant information from them. You might also want to generate some simulations, asking for specific KPIs but also generating reports about the main relevances caught in data. In fact, with so much structured data, you might also…

From code to natural language — Model interpretability refers to the ease with which a human can understand the reasoning behind the predictions made by a machine learning model. It is the ability to understand how a model is making decisions and what factors are contributing to its predictions. There are many reasons why model interpretability…

Overview of language models and hyperparameters — OpenAI and its disruptive development of generative models are probably the most trending topic in the Tech industry today. With good reason. The company indeed aims to promote and develop friendly AI in a way that benefits humanity as a whole. …

Part 1: Pre-built features — Azure Cognitive Services is a collection of cloud-based APIs that allow developers to add intelligent features to their applications. …

Part 10: Predictions — We got to the final Part of this series of articles, where I will use ML models to classify Judicial Sentences into categories. For this purpose, I will perform a supervised learning task using as a training set the already-labeled records, in order to apply the models to those records…

Part 9: Unsupervised Sentiment Analysis — Sentiment analysis is a natural language processing technique that involves using machine learning algorithms to identify and extract subjective information from text data. It is commonly used to determine the overall sentiment of a piece of writing, whether it is positive, negative, or neutral. This can be useful for a…