Generative Model vs  Discriminative Model

Generative AI Model: GPT-4

Generative AI models create new data resembling the training data. An example is GPT-3, which generates human-like text based on the input it receives.

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How GPT-4 Works

GPT-4 generates coherent and contextually relevant text by predicting the next word in a sentence, learning from vast amounts of data to mimic human language patterns.

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Discriminative AI Model: Logistic Regression

Logistic Regression is a classic discriminative AI model. It predicts categorical outcomes, such as spam or not spam, by learning the boundary between classes.

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How Logistic Regression Works

Logistic Regression estimates the probability of a data point belonging to a particular class using input features, optimizing the decision boundary for classification tasks.

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Generative models like GPT-4 are used in creative writing, chatbots, and content generation, where producing human-like text is essential for enhancing user experiences.

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Applications of Generative Models

Applications of Discriminative Models

Discriminative models like Logistic Regression are utilized in spam detection, fraud detection, and medical diagnosis, where accurately classifying data into categories is crucial.

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