eugenesiow / practical-ml

Learn by experimenting on state-of-the-art machine learning models and algorithms with Jupyter Notebooks.

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Practical Machine Learning

Learn by experimenting on state-of-the-art machine learning models and algorithms.

πŸ“– Table of Contents

↑ Introduction

"Progress is a natural result of staying focused on the process of doing anything." - Thomas Sterner, The Practicing Mind

Pratical ML is a collection of Jupyter notebooks where one can learn by example and actively practice training state-of-the-art machine learning models and algorithms.

To get started, find a task you are interested in below and hit the Open In Colab button on that row or hit the article πŸ“ button if you prefer to read instead.

↑ Computer Vision (CV)

Task Dataset Model πŸ“ Notebook
Anime Character GAN Private StyleGAN2 πŸ“ Open In Colab
Anime Super Resolution Private Waifu2x+CARN πŸ“ Open In Colab
Art Generation WikiArt v-diffusion+CLIP πŸ“ Open In Colab
Detect People From Images COCO YOLOv5 πŸ“ Open In Colab
Document Image Classification RVL-CDIP DiT πŸ“ Open In Colab
Face Super Resolution Private Real-ESRGAN πŸ“ Open In Colab
Face to Anime Dataset-1 AnimeGANv2 πŸ“ Open In Colab
Optical Character Recognition SROIE TrOCR πŸ“ Open In Colab
Remove Image Background VOC2012 DeepLabV3 πŸ“ Open In Colab

↑ Natural Language Processing (NLP)

Task Dataset SOTA SOTA Acc Our Acc πŸ“ Notebook
Hate Speech Detection Dynabench Leaderboard - 86.6 πŸ“ Open In Colab
Named Entity Recognition BC5CDR Nooralahzadeh et al. (2019) 89.9 89.3 πŸ“ Open In Colab
Named Entity Recognition CoNLL++ Wang et al. (2019) 94.3 93.5 πŸ“ Open In Colab
Named Entity Recognition (CN) MSRA Zhang et al. (2018) 93.2 93.9 πŸ“ Open In Colab
Named Entity Recognition (CN) WEIBO_1K Peng et al. (2016) 47 67.5 πŸ“ Open In Colab
Sarcarsm Detection Cai et al. (2019) Pan et al. (2020) 82.9 92.2 πŸ“ Open In Colab
Sentiment Analysis IMDB Yang et al. (2019) 96.2 92.2 πŸ“ Open In Colab
Sentiment Analysis (CN) WAIMAI_10K BERT 89 91.5 πŸ“ Open In Colab

↑ Speech

Task Dataset Model πŸ“ Notebook
Mandarin Text-to-Speech DataBaker Tacotron2-DDC-GST πŸ“ Open In Colab
Singlish Text-to-Speech IMDA FastSpeech2+MelGAN πŸ“ Open In Colab
Text-to-Speech LJ Speech Tacotron2+WaveGlow πŸ“ Open In Colab
Text-to-Speech Private SileroTTS πŸ“ Open In Colab
Video Subtitling LibriSpeech Wav2Vec2 πŸ“ Open In Colab
Video Subtitling Private Whisper πŸ“ Open In Colab

↑ Alternatives

↑ Contributors

Thanks goes to these wonderful people (emoji key):

This project follows the all-contributors specification. Contributions of any kind are welcome!

↑ License

MIT

↑ Citation

If you want to cite practical-ml, use the following Bibtex entry:

@misc{siow2020practicalml,
  title={Practical Machine Learning: A Collection of Machine Learning Experiments in Notebooks},
  author={Eugene Siow},
  year={2020},
  url={https://github.com/eugenesiow/practical-ml},
  note={Available at: https://github.com/eugenesiow/practical-ml}
}

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Learn by experimenting on state-of-the-art machine learning models and algorithms with Jupyter Notebooks.

License:MIT License


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