**4.1 TensorFlow**

The TensorFlow is new and open-source platform for differential programming; it was developed by Google team called Google brain and was first released in 2015 [24]. In February 2017, they released version 1.0.0; TensorFlow can work on CPU and GPU; it is available for Mac, Linux, and windows and also for mobile computing platform android and iOS. It is the most famous machine learning library in the world today. Its best-supported client language is python but there is also interface available in C++, Java, and GO. It is easy to use and have Keras integration. TensorFlow has many of its versions available like for mobiles TensorFlow lite, for industry TensorFlow Serving, etc.

## **4.2 Pytorch**

Pytorch is also machine learning and deep learning library, based on torch library. It was initially released by Facebook's AI Research lab (FAIR) in 2016. Pytorch has two high-level features, Tensor computing with graphics processing units (GPU), and auto-diff based deep neural network. It is too easy in Pytorch to move tensors to and from GPU. Pytorch Mobile is the version of Pytorch used for mobiles. There are some key features of Pytorch; the first feature is called imperative programming; most of the python code is imperative; this type of programming is more flexible. The other feature of Pytorch is dynamic computation graphs, it run time the system generates the graph structure, dynamic graph work well for dynamic networks like RNN, dynamic graph also makes debugging very easy. The Pytorch provides maximum flexibility and speed during implementing and building deep neural network.

#### **4.3 Theano**

Theano is designed by Montreal Institute for Learning Algorithms (MILA), which is very famous after their deployment, but unfortunately, there is no support after version 1.0.0 (November 2017). It is a python library designed for code compilation optimization [25]; it is primarily used for mathematical operations like multi-dimensional arrays. Theano was far better than other python libraries like Numpy in terms of speed, computing symbolic graphs, and stability optimizations. Tensor operations, GPU computation, and parallelism are also supported by Theano.
