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Gachon University
- Seoul, Korea
- https://www.linkedin.com/in/sungchul-choi-2aa1091b
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Tensors and Dynamic neural networks in Python with strong GPU acceleration
Models and examples built with TensorFlow
TensorFlow code and pre-trained models for BERT
PyTorch Tutorial for Deep Learning Researchers
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep lear…
Image augmentation for machine learning experiments.
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
A framework for training and evaluating AI models on a variety of openly available dialogue datasets.
Deep universal probabilistic programming with Python and PyTorch
Python Script to download hundreds of images from 'Google Images'. It is a ready-to-run code!
Ready-to-run Docker images containing Jupyter applications
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
A general-purpose encoder-decoder framework for Tensorflow
Collection of generative models in Tensorflow
Models, data loaders and abstractions for language processing, powered by PyTorch
A library for Multilingual Unsupervised or Supervised word Embeddings
Pandas integration with sklearn
PyTorch implementation of "Efficient Neural Architecture Search via Parameters Sharing"
Multi-layer Recurrent Neural Networks (LSTM, RNN) for character-level language models in Python using Tensorflow
Source-to-Source Debuggable Derivatives in Pure Python
Beautiful visualizations of how language differs among document types.
A curated list of pretrained sentence and word embedding models
Multi-Task Deep Neural Networks for Natural Language Understanding
Implementation of research papers on Deep Learning+ NLP+ CV in Python using Keras, Tensorflow and Scikit Learn.
PyTorch implementation of the NIPS-17 paper "Poincaré Embeddings for Learning Hierarchical Representations"
Google, Naver multiprocess image web crawler (Selenium)
The basic distribution probability Tutorial for Deep Learning Researchers