Large Language Models (LLMs) are advanced artificial intelligence models that are trained on massive amounts of text data to understand and generate human-like text.

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and act like humans. It encompasses a wide range of technologies and approaches, including machine learning, natural language processing, computer vision, and robotics, among others. AI systems are designed to perform tasks that typically require human intelligence, such[...]

Neural networks are a type of computing system inspired by the biological neural networks of animal brains. They consist of interconnected nodes, called neurons, which process and transmit information. Neural networks are a fundamental component of deep learning, a subset of machine learning that uses neural networks with many layers to learn from large amounts[...]

Deep learning is a subset of machine learning that uses neural networks with many layers (hence the term "deep") to learn from large amounts of data. Deep learning algorithms attempt to model high-level abstractions in data by using a deep graph with multiple processing layers, each of which transforms the input data to progressively more[...]

Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language in a way that is both meaningful and useful. NLP combines computational linguistics, computer science, and cognitive psychology to bridge the gap between human communication and computer understanding. NLP algorithms are designed to[...]

Supervised learning is a type of machine learning where the model is trained on a labeled dataset, meaning the input data is paired with the correct output. The goal of supervised learning is to learn a mapping from inputs to outputs, so that the model can make predictions on new, unseen data. In supervised learning,[...]