In this episode of Generative AI 101, we explore the core techniques and methods in Natural Language Processing (NLP). Starting with rule-based approaches that rely on handcrafted rules, we move to statistical models that learn patterns from vast amounts of data. We'll explain n-gram models and their limitations before diving into the revolution brought by machine learning, where algorithms like Support Vector Machines (SVMs) and decision trees learn from annotated datasets. Finally, we arrive at deep learning and neural networks, particularly Transformers, which enable advanced models like BERT and GPT-3 to understand context and generate human-like text.

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