My code implementations for the book Deep Learning for Coders with fastai and PyTorch
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bear_classifier - Classification on a custom data set using Fastai
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fastai_pytorch - Training a model by combining PyTorch and Fastai
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multilabel_fastai - Resnet18 model trained for a multi-label classification task using Fastai
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pet_breed_multiclass - Made a multi-label classifier using Fastai using pretrained Resnet18 model
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image_regression - A model to predict the centre point of the face
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computer_vision_techniques - Techniques for improving the accuracy of computer vision models
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cyclicLR_scratch - Implemented the cyclic learning rate for training neural networks as discussed in the paper Cyclical Learning Rates for Training Neural Networks (from scratch)
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cyclicLR_pytorch - Trained a model using the cyclic learning rate scheduler present in the PyTorch library
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fastai_collab - Collaborative filtering for movie recommendation using Fastai and PyTorch
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NLP_1 - Provides a high level overview of different tokenization approaches and also introduces to ULMFIT(Universal Language Model FIne-Tuning) technique for transfer learning in NLP
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text_preprocessing - Text preprocessing techniques like tokenization, stemming and lemmatization using spacy and NLTK
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RNN_fastai_pytorch - Building an RNN(Recurrent Neural Network) from scratch
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text_spacy_nltk - Text preprocessing techniques like tokenization, lemmatization, parts of speech tagging, named entity recognition and visualizing them using spacy
If you wish to experiment with every notebook in this repo, Launch Binder