hamaadshah/gan_deeplearning4j
Automatic feature engineering using Generative Adversarial Networks using Deeplearning4j and Apache Spark.
This project helps data scientists and machine learning engineers automatically create better features for their models, especially when dealing with noisy, raw data. It takes in existing raw data, like financial transactions or image data, and generates new, more informative features that can improve the performance of supervised learning tasks. This is ideal for those building predictive models in areas like computer vision or finance.
No commits in the last 6 months.
Use this if you need to improve the predictive power of your machine learning models by automatically extracting more meaningful features from complex or noisy raw datasets.
Not ideal if you already have a well-defined set of features and are not facing challenges with raw data processing or feature extraction.
Stars
21
Forks
8
Language
Java
License
Apache-2.0
Category
Last pushed
Jul 15, 2022
Commits (30d)
0
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