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Framework and examples for active learning with machine learning model
Active Learning is a Python-based research framework developed by Google for experimenting with and benchmarking various active learning algorithms. It provides modular tools for running reproducible experiments across different datasets, sampling strategies, and machine learning models. The system allows researchers to study how models can improve labeling efficiency by selectively querying the most informative data points rather than relying on uniformly sampled training sets. The main...
Belkerda is a simple Python AI program that takes a user's input, builds a log of random numbers, picks a random entry, and displays it. If it is correct, then it reenters that number back into the log several times, overwriting the original, random numbers. If it is not, however, it overwrites a lower amount of entries.
ngram is a module to compute the similarity between two strings. It is different to python's "difflib.SequenceMatcher" in that it cares more about the size of both strings. ngram is an port and extension of the perl module called "String::Trigram
Generic engine to filter information.
We wish to show that the power of expression of a filter makes it possible to appreciably reduce the size of the code necessary to extract information and that it is possible in Python.
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