📘 Introduction Artificial Intelligence is evolving rapidly. From rule-based systems to generative models, we are now entering a new frontier: Agentic AI. But what exactly is Agentic AI? How does it differ from traditional AI systems, and what opportunities or risks does it bring? In this post, we'll explore...
📘Introduction Large language models (LLMs) have revolutionized artificial intelligence, unlocking new levels of understanding and creativity. But AI’s journey is far from overnight — it’s been shaped by decades of innovation, setbacks, and breakthroughs. In this post, we’ll explore the pivotal moments in AI’s history — from the...
Introduction Retrieval-Augmented Generation (RAG) is an advanced technique in natural language processing that enhances language models by enabling them to retrieve external information in real time. This leads to more accurate and context-aware responses, making RAG ideal for tasks that require up-to-date or domain-specific knowledge, such as customer support, enterprise...
Introduction The Hugging Face Inference API makes it easy to send prompts to large language models (LLMs) hosted on the Hugging Face Hub. By combining this with Gradio, you can quickly build interactive chatbots and demos with a simple, web-based user interface—without worrying about backend frameworks. In this tutorial,...
Introduction The Hugging Face Inference API makes it easy to send prompts to large language models (LLMs) hosted on the Hugging Face Hub. By combining this with FastAPI—a modern Python web framework—you can build scalable, production-ready APIs that serve LLM-powered responses to your applications. In this tutorial, you’...
Introduction Autoencoders, a cornerstone of unsupervised learning, have emerged as a powerful tool in the domain of artificial intelligence. By compressing and then reconstructing input data, these neural networks uncover meaningful representations, paving the way for applications ranging from image denoising to anomaly detection. In this tutorial we want to...
Introduction Traditional RNNs, limited by their simplistic structure, have problems retaining information over longer time periods, leading to the infamous vanishing gradient problem. Long Short-Term Memory (LSTM) Networks have the impressive ability to capture and preserve long-term dependencies in sequential data. But how is an LSTM able to do this?...
Introduction In 1997, Sepp Hochreiter and Jürgen Schmidhuber introduced Long Short-Term Memory (LSTM), a special type of Recurrent Neural Network (RNN). The motivation was to overcome the problems of traditional RNNs when dealing with long sequences. This is exactly what LSTM Networks (LSTMs) are supposed to do. The special architecture...
Introduction In this tutorial we will cover how to represent boolean functions with the McCulloch-Pitts Neuron. We look at the boolean functions AND, OR and NOT. McCulloch-Pitts Neuron We have already explained how the McCulloch-Pitts Neuron works in another post. If you haven't seen it yet, check it...