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Code implementations for the book Deep Learning for Coders with fastai and PyTorch

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My code implementations for the book Deep Learning for Coders with fastai and PyTorch


  1. bear_classifier - Classification on a custom data set using Fastai

    Open In Colab

  2. fastai_pytorch - Training a model by combining PyTorch and Fastai

    Open In Colab

  3. multilabel_fastai - Resnet18 model trained for a multi-label classification task using Fastai

    Open In Colab

  4. pet_breed_multiclass - Made a multi-label classifier using Fastai using pretrained Resnet18 model

    Open In Colab

  5. image_regression - A model to predict the centre point of the face

    Open In Colab

  6. computer_vision_techniques - Techniques for improving the accuracy of computer vision models

    Open In Colab

  7. 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)

    Open In Colab

  8. cyclicLR_pytorch - Trained a model using the cyclic learning rate scheduler present in the PyTorch library

    Open In Colab

  9. fastai_collab - Collaborative filtering for movie recommendation using Fastai and PyTorch

    Open In Colab

  10. 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

    Open In Colab

  11. text_preprocessing - Text preprocessing techniques like tokenization, stemming and lemmatization using spacy and NLTK

    Open In Colab

  12. RNN_fastai_pytorch - Building an RNN(Recurrent Neural Network) from scratch

    Open In Colab

  13. text_spacy_nltk - Text preprocessing techniques like tokenization, lemmatization, parts of speech tagging, named entity recognition and visualizing them using spacy

    Open In Colab


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Code implementations for the book Deep Learning for Coders with fastai and PyTorch

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