In the earlier days, people needed to have expertise in C++ and CUDA to implement DL algorithms. With a lot of organizations now open sourcing their deep learning frameworks, people with knowledge of a scripting language, such as Python, can start building and using DL algorithms. Some of the popular deep learning frameworks used today in the industry are TensorFlow, Caffe2, Keras, Theano, PyTorch, Chainer, DyNet, MXNet, and CNTK.

The adoption of deep learning would not have been this huge if it had not been for these frameworks. They abstract away a lot of underlying complications and allow us to focus on the applications. We are still in the early days of DL where, with a lot of research, breakthroughs are happening every day across companies and organizations. As a result of this, various frameworks have their own pros and cons.