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DeepaiDetection of Accounting Anomalies using Deep Autoencoder Neural Networks - A lab we prepared for NVIDIA's GPU Technology Conference 2018 that will walk you through the detection of accounting anomalies using deep autoencoder neural networks. The majority of the lab content is based on Jupyter Notebook, Python and PyTorch.
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Generative ModelsAnnotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN
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KateCode & data accompanying the KDD 2017 paper "KATE: K-Competitive Autoencoder for Text"
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Collaborative Deep Learning For Recommender SystemsThe hybrid model combining stacked denoising autoencoder with matrix factorization is applied, to predict the customer purchase behavior in the future month according to the purchase history and user information in the Santander dataset.
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RecoderLarge scale training of factorization models for Collaborative Filtering with PyTorch
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SmrtHandle class imbalance intelligently by using variational auto-encoders to generate synthetic observations of your minority class.
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DeeptimeDeep learning meets molecular dynamics.
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ZhihuThis repo contains the source code in my personal column (https://zhuanlan.zhihu.com/zhaoyeyu), implemented using Python 3.6. Including Natural Language Processing and Computer Vision projects, such as text generation, machine translation, deep convolution GAN and other actual combat code.
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CalcConvolutional Autoencoder for Loop Closure
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SplitbrainautoSplit-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction. In CVPR, 2017.
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AialphaUse unsupervised and supervised learning to predict stocks
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