2 dépôts
Applies custom functions or regex patterns to filenames to derive labels for supervised learning.
Distinct from Custom Dataset Loading: Distinct from Custom Dataset Loading: focuses on extracting labels from filenames, not general data reading logic.
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Snorkel is a weak supervision system that enables users to programmatically generate training labels for machine learning models without manual annotation. At its core, it provides a framework for writing labeling functions as Python callables that each vote on data points, and then trains a probabilistic graphical model over these multiple weak supervision sources to estimate latent true labels without any ground truth data. The system automatically learns accuracy and correlation parameters between labeling functions by analyzing observed agreement patterns on unlabeled data, converting lab
Provides a framework for writing Python callables that vote on data points to programmatically label datasets.
This is a structured deep learning curriculum for programmers, delivered as a collection of Jupyter notebooks. It teaches the fundamentals of training neural networks for computer vision, natural language processing, tabular data analysis, and collaborative filtering using PyTorch and the fastai library. The course is designed to be hands-on, guiding learners from building a training loop from scratch to fine-tuning pretrained models for a variety of practical tasks. The curriculum distinguishes itself by covering the full lifecycle of a deep learning project, from data preparation and augmen
Ships utilities for extracting labels from image filenames using custom functions or regex.