Deep Learning using TensorFlow and R: A Step-by-step Tutorial

Deep learning, also known as deep structured learning or hierarchical learning, is a type of machine learning focused on learning data representations and feature learning rather than individual or specific tasks. Feature learning, also known as representation learning, can be supervised, semi-supervised or unsupervised. Deep learning architectures include deep neural networks, deep belief networks and recurrent neural networks. Real-world applications using deep learning include computer vision, speech recognition, machine translation, natural language processing, and image recognition. The following recipe introduces how to implement a deep neural network using TensorFlow, which is an open source software library, originally developed at Google, for complex computation by constructing network graphs of mathematical operations and data (Abadi et al. 2016; Cheng et al. 2017).