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Data preparation: This step includes the following tasks: data preprocessing, data cleaning, and exploratory data analysis (EDA). For text data, we would convert text data features into vectors and perform Tokenization, Stemming, and Lemmatization, as well as other possible steps described in NaturalLanguageProcessing on my GitHub repo.
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.
Transformers Originally developed for naturallanguageprocessing, transformer models have been adapted for Time Series Forecasting due to their ability to capture complex relationships across long sequences of data. Split the Data: Divide your dataset into training, validation, and testing subsets to ensure robust evaluation.
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.
Cross-Validation: A model evaluation technique that assesses how well a model will generalise to an independent dataset. Exploratory Data Analysis (EDA): Analysing and visualising data to discover patterns, identify anomalies, and test hypotheses. NLP enables machines to understand and interpret text and speech.
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