We have almost come to the end of our journey in deep learning with R. This chapter is a bit of a mixed bag of topics. We will begin this chapter by revisiting an image classification task and building a complete image classification solution image files rather than tabular data. We will then move on to explaining transfer learning, where you can use an existing model on a new dataset. Next we discuss an important consideration in any machine learning project - how will your model be used in deployment, that is, production? We will show how to create a REST API that allows any programming language to call a deep learning model in R to predict on new data. We will then move on to briefly discussing two other deep learning topics: Generative Adversarial Networks and reinforcement learning. Finally, we will close this chapter and the book by providing...
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