PyTorch

PyTorch, and most of the other deep learning frameworks, can be used for two different things:

• Replacing NumPy-like operations with GPU-accelerated operations
• Building deep neural networks

What makes PyTorch increasingly popular is its ease of use and simplicity. Unlike most other popular deep learning frameworks, which use static computation graphs, PyTorch uses dynamic computation, which allows greater flexibility in building complex architectures.

PyTorch extensively uses Python concepts, such as classes, structures, and conditional loops, allowing us to build DL algorithms in a pure object-oriented fashion. Most of the other popular frameworks bring their own programming style, sometimes making it complex to write new algorithms and it does not support intuitive debugging. In the later chapters, we will discuss computation graphs in detail.

Though PyTorch was released recently and is still in its beta version, it has become immensely popular among data scientists and deep learning researchers for its ease of use, better performance, easier-to-debug nature, and strong growing support from various companies such as SalesForce. 

As PyTorch was primarily built for research, it is not recommended for production usage in certain scenarios where latency requirements are very high. However, this is changing with a new project called Open Neural Network Exchange (ONNX) (https://onnx.ai/), which focuses on deploying a model developed on PyTorch to a platform like Caffe2 that is production-ready. At the time of writing, it is too early to say much about this project as it has only just been launched. The project is backed by Facebook and Microsoft.

Throughout the rest of the book, we will learn about the various Lego blocks (smaller concepts or techniques) for building powerful DL applications in the areas of computer vision and NLP.