A Tour of TensorFlow

Abstract

Deep learning is a branch of artificial intelligence employing deep neural network architectures that has significantly advanced the state-of-the-art in computer vision, speech recognition, natural language processing and other domains. In November 2015, Google released $\text{TensorFlow}$, an open source deep learning software library for defining, training and deploying machine learning models. In this paper, we review TensorFlow and put it in context of modern deep learning concepts and software. We discuss its basic computational paradigms and distributed execution model, its programming interface as well as accompanying visualization toolkits. We then compare TensorFlow to alternative libraries such as Theano, Torch or Caffe on a qualitative as well as quantitative basis and finally comment on observed use-cases of TensorFlow in academia and industry.

References in corpus (5)

• Scikit-learn: Machine Learning in Python
• Caffe: Convolutional Architecture for Fast Feature Embedding
• MLlib: Machine Learning in Apache Spark
• Using Recurrent Neural Networks to Optimize Dynamical Decoupling for Quantum Memory
• A Tutorial on Principal Component Analysis with the Accord.NET Framework

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