In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.
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Please help with GAN and reinforcement learning #228
My and my friend are preparing applications for graduate school and we are so interested in the artificial intelligence area and we decide to make a project related to that. Then we found your paper and was shocked by its complexity yet reasonable analysis.
Your work about GAN and reinforcement learning on hyperparameters was quite attractive! I am really interested in learning how a genius like you think this kind of problem and try to find related patterns to apply to it.
Could you please share with me the reinforcement learning idea and perhaps also the GAN code and deep reinforcement learning code that you used to analyze the stock data? I would sincerely appreciate your help!
Thank you very much,
Frank Kang
kynkangyining@gmail.com
Hi Boris,
My and my friend are preparing applications for graduate school and we are so interested in the artificial intelligence area and we decide to make a project related to that. Then we found your paper and was shocked by its complexity yet reasonable analysis.
Your work about GAN and reinforcement learning on hyperparameters was quite attractive! I am really interested in learning how a genius like you think this kind of problem and try to find related patterns to apply to it.
Could you please share with me the reinforcement learning idea and perhaps also the GAN code and deep reinforcement learning code that you used to analyze the stock data? I would sincerely appreciate your help!
Thank you very much, Frank Kang kynkangyining@gmail.com