Deep learning, one of the most fascinating subjects in computer science, has spawned a slew of machine learning frameworks and libraries, sparking community discussions about platforms like PyTorch vs TensorFlow. Currently, the most prominent frameworks are PyTorch and TensorFlow, which were created by Facebook and Google, respectively. Both of these frameworks are open-source libraries for machine learning that are widely utilised in commercial and academic research. They're also distinct enough that you'll want to think about the framework you'll use before getting started. Why is there a debate between PyTorch and TensorFlow in the machine learning community?

They are the scaffolding of the machine learning revolution: the widespread adoption of deep learning frameworks like TensorFlow and PyTorch enabled many ML practitioners to more easily assemble models using well-suited domain-specific languages and a rich collection of building blocks. Looking back at the evolution of deep learning frameworks we can clearly see a tightly coupled relationship between deep learning frameworks and deep learning algorithms. The concept of neural networks have been around for a while. Before the early 2000s, there were a handful of tools that can be used to describe and develop neural networks. These tools include MATLAB, OpenNN, and Torch etc.

The past decade has seen a burst of algorithms and applications in machine learning especially deep learning. Behind the burst of these deep learning algorithms and applications are a wide variety of deep learning tools and frameworks. They are the scaffolding of the machine learning revolution: the widespread adoption of deep learning frameworks like TensorFlow and PyTorch enabled many ML practitioners to more easily assemble models using well-suited domain-specific languages and a rich collection of building blocks. Looking back at the evolution of deep learning frameworks we can clearly see a tightly coupled relationship between deep learning frameworks and deep learning algorithms. The concept of neural networks have been around for a while. Before the early 2000s, there were a handful of tools that can be used to describe and develop neural networks.

As the success of AlexNet drew great attention in the area of computer vision and reignited the hope of neural networks, large tech companies joined the force of developing deep learning frameworks. Among them, Google open sourced the famous TensorFlow framework that is still the most popular deep learning framework in ML field up to date. The inventor of Caffe joined Facebook and continued the release of Caffe2; at the same time, Facebook AI Research (FAIR) team also released another popular framework PyTorch which was based on the Torch framework but with the more popular Python APIs. Microsoft Research developed the CNTK framework. And Amazon adopted MXNet, a joint academic project from University of Washington, CMU and others.