Keras, a popular library for specifying deep learning models has now been directly incorporated into TensorFlow via the tf.keras high level deep learning API. Since then the situation has improved further. In the previous article on the same topic we discussed how sophisticated quantitative trading research with machine learning requires a robust framework to abstract away the machine learning model specification from the model implementation.Īt the time of the original article the TensorFlow library provided such an abstraction by avoiding the need to write optimised deep learning models in low-level C, C++ or FORTRAN and the CUDA GPU programming model provided by Nvidia. In this article we will demonstrate how to install a modern deep learning research environment on a Linux machine via the TensorFlow library, which will form the basis of all subsequent deep learning research on QuantStart. This article constitutes the first in a series on the topic of modern machine learning via deep learning as applied to systematic trading research. Earlier in the year we carried out our 2020 QuantStart Content Survey and Advanced Machine Learning & Deep Learning was voted the most popular topic.
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