From the course: Python for AI Projects: From Data Exploration to Impact

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Model fitting

Model fitting - Python Tutorial

From the course: Python for AI Projects: From Data Exploration to Impact

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Model fitting

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- [Instructor] Let's talk about model fitting, the core step where a machine learning model actually learns from data. In traditional NLP pipelines and other supervised learning tasks, this means training a model to map inputs to outputs using labeled examples and optimization algorithms. But with LLMs, most of the learning happens upstream long before we ever interact with the model. These models are typically trained on massive collections of text using self-supervised learning, a technique where the model learns by predicting parts of the text from other parts, such as filling in missing words or generating the next sentence. Through this process, the model absorbs language structure, world knowledge, and reasoning patterns, all without requiring manually labeled data. This phase is known as pre-training, and it can involve trillions of tokens and billions of parameters, requiring enormous computational resources.…

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