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I am working on machine/deep learning projects related to different domains: natural language processing, natural language understanding, computer vision, recommender systems, medical databases. I have got an applied knowledge in implementation of various machine learning algorithms: feed-forward neural networks, convolutional neural networks, recurrent neural networks, neural attention mechanism, encoder-decoder architectures and generative adversarial networks. I have got a PhD application in Electronics field. My thesis concerned the analysis of subset of various feature selection algorithms in order to select the most significant attributes with solid distinctive abilities from medical databases.

Featured work

  1. tlatkowski/inpainting-gmcnn-keras

    Keras implementation of "Image Inpainting via Generative Multi-column Convolutional Neural Networks" paper published at NIPS 2018

    Python 93
  2. tlatkowski/deep-learning-gene-expression

    Deep learning methods for feature selection in gene expression autism data.

    Jupyter Notebook 34
  3. tlatkowski/multihead-siamese-nets

    Implementation of Siamese Neural Networks built upon multihead attention mechanism for text semantic similarity task.

    Jupyter Notebook 183
  4. tlatkowski/tf-feature-selection

    Implementation of various feature selection methods using TensorFlow library.

    Python 9
  5. tlatkowski/neural-recommender

    Neural recommender system implementation in TensorFlow.

    Python 16
  6. tlatkowski/gans-2.0

    Generative Adversarial Networks in TensorFlow 2.0

    Python 78

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