Jupyter notebooks that use the Fastai library
Following our publication of the WikiExtractor.py file which is platform-independent (ie running on all platforms, especially Windows), we publish our nlputils2.py file, which is the platform-independent version of the nlputils.py file of the fastai NLP course (more: we have split the original methods into many to use them separately and we have added one that cleans a text file).
The extraction script WikiExtractor.py does not work when running fastai on Windows 10 because of the 'utf-8' encoding that is platform-dependent default in the actual code of the file.
Thanks to Albert Villanova del Moral that did the pull request "Force 'utf-8' encoding without relying on platform-dependent default" (but not merged until now (31st of August, 2019) by the script author Giuseppe Attardi), we know how to change the code. Thanks to both of them!
Links:
- Original WikiExtractor (but not updated with platform independent code)
- Updated WikiExtractor from Albert Villanova del Mora (UPDATED !!!)
- My file WikiExtractor.py saved here with the platform independent code (ie, working on all platforms and in particular on Windows)
O Hackathon Brasal/PCTec-UnB 2019 foi uma maratona de dados (dias 9 e 10 de maio de 2019), que reuniu estudantes, profissionais e comunidade, com o desafio de em dois dias, realizaram um projeto de Bussiness Intelligence para um cliente real: Brasal Veículos. Aconteceu no CDT da Universidade de Brasília (UnB) no Brasil. Nesse contexto, minha equipe desenvolveu o projeto "Vendedor IA" (VIA), um conjunto de modelos de Inteligência Artificial (IA) usando o Deep Learning cujo princípio é descrito nos 2 jupyter notebooks que foram criados:
- Data clean (vendas_veiculos_brasal_data_clean.ipynb): é o notebook de preparação da tabela de dados de vendas para treinar os modelos do VIA.
- Regressão (vendedor_IA_vendas_veiculos_brasal_REGRESSAO.ipynb): é o notebook de treinamento do modelo que fornece o orçamento que o cliente está disposto a gastar na compra de um veículo.
The objective of the jupyter notebook MURA | Abnormality detection is to show how the fastai v1 techniques and code allow to get a top-level classifier in the world of health. [ NEW ] We managed to increase our kappa score in this notebook (part 2).
It is an images classifier that use the Deep Learning model resnet (the resnet50 version) that won the ImageNet competition in 2015 (ILSVRC2015). It classifies an image into 1000 categories.
The objective of the jupyter notebook pretrained-imagenet-classifier-fastai-v1.ipynb is to use fastai v1 instead of Pytorch code in order to classify images into 1000 classes by using an ImageNet winner model.
The jupyter notebook data-augmentation-by-fastai-v1.ipynb presents the code to apply transformations on images with fastai v1.
The jupyter notebook lesson1-quick.ipynb is an exercise that was proposed on 17/04/2018 & 21/04/2018 to the participants of the Deep Learning study group of Brasilia (Brazil). Link to the thread : http://forums.fast.ai/t/deep-learning-brasilia-revisao-licoes-1-2-3-e-4/14993
The jupyter notebook lesson1-DogBreed.ipynb is an exercise that was proposed on 17/04/2018 & 21/04/2018 to the participants of the Deep Learning study group of Brasilia (Brazil). Link to the thread : http://forums.fast.ai/t/deep-learning-brasilia-revisao-licoes-1-2-3-e-4/14993
https://github.com/piegu/fastai-projects/blob/master/howto_make_predictions_on_test_set