A deep learning platform provides a set of tools and an interface for building custom deep nets. Typically they provide a user with a selection of deep nets to choose from, along with the ability to integrate data from different sources, manipulate data, and manage models through user interface. Some platforms also help with performance if a net needs to be trained with a large dataset. The platform is typically an out of the box application that lets us configure a deep net’s hyper-parameters through an intuitive UI. With a platform, we don’t need to know anything about coding aspects in order to use tools. The downside is that we are constrained by the platform’s selection of deep nets as well as the configuration options. However, for somebody to quickly deploy a deep net, a platform could be the best way to go. Deep learning platforms come in two different forms – software platform and full platform. Examples are H2O.ai, Data Graph etc Library is a set of functions and modules that we can call through our own programs in order to perform certain tasks. Deep net libraries gives us lot of flexibility with net selection and hyper-parameter configuration. For example: there are not many platforms that let us build a Recursive Neural Tensor Net (RNTN), however we can code our own with the appropriate deep net library. The obvious downside here is the coding experience required to use them. However if we need flexibility, these are great options. Examples are TensorFlow, Theano, Caffe, deeplearning4j, torch etc.