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Deep Learning has been used to achieve state-of-the-art results in a variety of tasks, including image recognition, NaturalLanguageProcessing, and speech recognition. NaturalLanguageProcessing (NLP) This is a field of computer science that deals with the interaction between computers and human language.
These networks can learn from large volumes of data and are particularly effective in handling tasks such as image recognition and naturallanguageprocessing. Key Deep Learning models include: Convolutional Neural Networks (CNNs) CNNs are designed to process structured grid data, such as images.
This extensive repertoire includes classification, regression, clustering, naturallanguageprocessing, and anomaly detection. The compare_models() function trains all available models in the PyCaret library and evaluates their performance using cross-validation, providing a simple way to select the best-performing model.
LLMs are one of the most exciting advancements in naturallanguageprocessing (NLP). Part 1: Training LLMs Language models have become increasingly important in naturallanguageprocessing (NLP) applications, and LLMs like GPT-3 have proven to be particularly successful in generating coherent and meaningful text.
Naturallanguageprocessing ( NLP ) allows machines to understand, interpret, and generate human language, which powers applications like chatbots and voice assistants. Predictive analytics uses historical data to forecast future trends, such as stock market movements or customer churn.
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