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MusicComp: Original Music Composer

  • Developed a program which can compose original music pieces including several instruments such as piano, violin, and saxophone. ·

  • A generative adversarial neural network, whose discriminator and generator parts are recurrent neural networks (RNN-GAN), is utilized. ·

  • Developed program can also produce music according to the selected emotional theme. ·

  • Instruments which appear in music can also be selected by the user.

ImStyle: Painter Detection and Restyling

  • By using deep learning, developed a program that restyles the given image of the user according to the style of a painter selected (Van Gogh’s, Monet’s, etc.). ·

  • For the restyling process, deep convolutional generative adversarial networks (DC-GAN) are utilized. ·

  • Additionally, developed a program that predicts the painter of the given artistic piece by using convolutional neural networks (CNN).

Cryptocurrency Stock Prediction.

  • Developed a program that could predict the future stock prices of selected cryptocurrencies. ·

  • For prediction, I have trained a deep recurrent neural network with past values of cryptocurrencies obtained from ·

  • The accuracy of the program is 85% for the test data and 83% for the training data.

Chess Player

  • Developed a chess-playing program by using reinforcement learning which learned to play chess with trial and error. ·

  • I have used TensorFlow libraries in Python for coding deep reinforcement learning code. ·

  • As deep reinforcement learning method, deep recurrent Q-Learning (DRQN) was used.

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