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Understanding the Basics: Terminology and Concepts

With the rise of Artificial Intelligence, many key terms are being used more frequently and often interchangeably. Before delving into the details, it's crucial to establish a clear understanding by defining these terms.

• Artificial Intelligence (AI): The simulation of human intelligence by machines, especially computer systems, enabling tasks such as problem-solving, learning, and reasoning.
• Natural Language Processing (NLP): A branch of AI focused on enabling machines to understand, interpret, and generate human language. Example applications include speech recognition.
• Computer Vision (CV): A field of AI that focuses on enabling machines to interpret and understand visual information from the world, such as images and videos. It involves techniques to allow computers to extract meaningful data, recognize patterns, and make decisions based on visual input, mimicking human visual capabilities. Example applications include object detection and image classification.
• Machine Learning (ML): A subset of AI, focusing on the development of algorithms that allow machines to learn from data and improve their performance over time without explicit programming.
• Neural Networks (NN): NN is one type of ML algorithms. It is system of algorithms inspired by the human brain, consisting of layers of interconnected nodes (neurons) that process input data to generate outputs.
• Deep Learning (DL): A specialized subset of ML and NN that uses multiple layers of neural networks(deep neural networks) to model complex patterns in large datasets.

The following are some key definition that are commonly used in AI:

• Supervised Learning: A type of machine learning where the model is trained on labeled data, learning to map inputs to outputs.
• Classification: A type of supervised learning in ML where the goal is to assign input data to predefined categories or labels. A model is trained on labeled data to predict the category of new, unseen data based on learned patterns.
• Regression: Another type of supervised learning where the task is to predict continuous numerical values based on input data. It models the relationship between the input variables (features) and a continuous output variable.
• Unsupervised Learning: Involves training a model on unlabeled data, allowing it to identify patterns or groupings without explicit instructions.
• Reinforcement Learning: A learning paradigm where an agent learns by interacting with an environment, receiving feedback in the form of rewards or penalties.

The AI Landscape: Types and Categories

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