AI and Machine Learning Concepts

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Artificial Intelligence (AI)

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The theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

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Machine Learning (ML)

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A subfield of AI that allows computers to learn from data without being explicitly programmed. It focuses on building algorithms that can recognize patterns and make predictions or decisions.

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Neural Network

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A computational model inspired by the structure and function of the human brain. It consists of interconnected 'neurons' that process information in layers, allowing it to learn complex patterns. It is the foundational structure for Deep Learning.

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Supervised Learning

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A type of Machine Learning where the model is trained on a dataset that is labeled. The model's goal is to learn the mapping function that turns inputs into the correct outputs. (e.g., training with thousands of images labeled 'cat' or 'dog').

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Unsupervised Learning

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A type of Machine Learning where the model is trained on an unlabeled dataset. The model's goal is to find hidden patterns and structures in the data on its own. (e.g., clustering news articles by topic without being told the topics in advance).

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Reinforcement Learning

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A type of Machine Learning where an 'agent' learns to make decisions by performing actions in an environment to achieve a reward. It learns through trial and error, like training a dog with treats.

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Deep Learning

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A subfield of Machine Learning that uses deep neural networks (with many layers) to learn from vast amounts of data. The depth allows it to learn highly complex patterns, from identifying objects in an image to understanding human speech.

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Transformer Architecture

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A powerful and efficient neural network design that has become the foundation for most modern AI models. Its key innovation is 'self-attention,' which allows it to weigh the importance of different pieces of input data to understand context.

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Large Language Model (LLM)

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A type of AI built using a Transformer architecture and trained on massive amounts of text data. This extensive training enables it to understand, generate, summarize, and translate human language with remarkable fluency.

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Diffusion Model

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A type of generative AI model, primarily used for creating high-quality images. It learns to reverse a process of adding random 'noise' to an image, allowing it to generate a new image from scratch by starting with noise and progressively refining it.