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Last Commit
Oct. 18, 2017
Mar. 12, 2017

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The following command clones all the files (>200MB);

git clone

Any test file can be run directly, the contents below are used to index the model and test file;


I have used these python-based libraries in different sections:

Style of My Code

I write most models in scikit-learn interface style, with fit() and predict() methods, and then write separate test files for different incoming data;


Machine Learning

Linear Model

  • TensorFlow   |   Linear Regression     Model     Test   |  

  • TensorFlow   |   Logistic Regression     Model     Test   |  

  • TensorFlow   |   Support Vector Machine     Model     Test   |  

  • Java   |   Logistic Regression     Model     Test   |  

  • Java   |   Support Vector Machine     Model     Test   |  

Non-Linear Model


  • Python   |   Bagging     Model     Test   |  

  • Python   |   Adaboost     Pseudocode     Model     Test   |  

  • Python   |   Random Forest     Model     Test   |  

Deep Learning

Multilayer Perceptron

Convolutional Network

Recurrent Network


Highway Network

  • TensorFlow   |   MLP Highway Classifier     Model     MNIST Test   |  

  • TensorFlow   |   Conv1D Highway Classifier     Model     IMDB Test   |  

Generative Adversarial Network

Reinforcement Learning

Natural Language Processing

Text Representation

Text Classification

Text Generation

Text Labelling

Text to Text

Image To Text

(To run this section, you need to download COCO dataset first)

  • TensorFlow   |   CNN + RNN     Model     COCO Test   |  

  • TensorFlow   |   CNN + RNN + Attention + Beam-Search     Model     COCO Test   |  

  • TensorFlow   |   Fine-tuning CNN + RNN + Attention + Beam-Search     Model     COCO Test   |  

Information Retrieval

Recommendation System

Computer Vision


Cloud Computing

Apache Spark