Remove Cross Validation Remove EDA Remove Natural Language Processing
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The AI Process

Towards AI

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 Natural Language Processing on my GitHub repo.

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Artificial Intelligence Using Python: A Comprehensive Guide

Pickl AI

Deep Learning has been used to achieve state-of-the-art results in a variety of tasks, including image recognition, Natural Language Processing, and speech recognition. Natural Language Processing (NLP) This is a field of computer science that deals with the interaction between computers and human language.

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AI in Time Series Forecasting

Pickl AI

Transformers Originally developed for natural language processing, 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.

AI 52
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Large Language Models: A Complete Guide

Heartbeat

LLMs are one of the most exciting advancements in natural language processing (NLP). Part 1: Training LLMs Language models have become increasingly important in natural language processing (NLP) applications, and LLMs like GPT-3 have proven to be particularly successful in generating coherent and meaningful text.

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Basic Data Science Terms Every Data Analyst Should Know

Pickl AI

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.