From personalizing customer experience to automating processes, Deep Learning applications are offering smart solutions to businesses across industries, opening up a world of opportunities for them. Deep Learning algorithms use sophisticated structures, such as Convolutional Neural Networks, belief networks, or recurrent neural networks. Effective DL frameworks also help simplify the implementation of large and complex models like Convolutional Neural Networks. In this post, we present the top Deep Learning frameworks preferred by data scientists and Deep Learning experts across the globe. We have also included the major pros and cons of each framework, enabling you to choose the right one for your upcoming project.

For workstation development platforms purpose-built for Tensorflow, PyTorch, Caffe2, MXNet, and other DL frameworks, the solution is BOXX.