your own innovation and creativity. Dealing with the problem of an ever-changing market. Next, head over to nvidias GPU documentation, located at m/cuda-gpus. Keras also plays nicely with CPUs and GPUs and can integrate with the TensorFlow, Theano and cntk backends without limiting the flexibility of those tools. Step 1: What hardware do you have? You can read all about cuDNN here. This is the first in a multi-part series in which we explore and compare various deep learning tools and techniques for market forecasting using Keras and TensorFlow. Keras is a high-level API for building and training neural networks.
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In the last few years, deep learning has gone from being an interesting but impractical academic pursuit to a ubiquitous technology that touches many aspects of our lives on a daily basis including in the world of trading. The speed up in model training is really significant. This is the second in a multi-part series in which we explore and compare various deep learning tools and techniques for market forecasting using Keras and TensorFlow. Said differently, feeding market data to a machine learning algorithm is only useful to the extent that the past is a predictor of the future. We will do so using only historical market data, so the results need to be interpreted considering the discussion above. Having activated your nvidia developers account, youll need to download the correct version of cuDNN.
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