postrou/hyperparameter_optimization

Code for article on hyperparameter optimization

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This project helps machine learning practitioners fine-tune their natural language processing models more efficiently. By taking raw movie review text data and a neural network model as input, it demonstrates how different optimization methods can quickly find the best settings for the model. The output shows which model configurations achieve the highest accuracy in classifying sentiment, saving time and computational resources for data scientists and ML engineers.

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Use this if you are a data scientist or ML engineer working with NLP tasks and need to systematically find the best parameters for your deep learning models without extensive manual trial and error.

Not ideal if you are not working with deep learning models, specifically in natural language processing, or if you need to optimize parameters for traditional machine learning algorithms outside of neural networks.

natural-language-processing machine-learning-engineering model-optimization sentiment-analysis deep-learning
No License Stale 6m No Package No Dependents
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Jupyter Notebook

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Last pushed

Aug 17, 2022

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