Tensorflow is a open-source machine learning (ML) framework. It’s primarily used to build neural networks, and thus very often used to conduct so-called deep learning through multi-layered neural nets.
Although there are other ML frameworks — such as Caffe or Torch — Tensorflow is particularly famous because it was developed by researchers of Google’s Brain Lab. There are widespread debates on which framework is best, nonetheless, Tensorflow does a pretty good job on marketing itself.
I primarily work in the programming language R, and have written before about how to start with deep learning in R using Keras — an user-friendly API built on top of, among others, Tensorflow. Now, it has become even easier to learn how to implement the power of Tensorflow in R, for RStudio has compiled a gallery of featured posts on Tensorflow implementations in R. It features a variety of applications related to collaborative filtering, image recognition, audio classification, times series forecasting, and fraud detection, all using Keras and TensorFlow. I highly recommend you check it out if you want to learn more about deep learning in R.
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