PyTorch For Deep Learning
PyTorch is a well-known deep learning framework which is used to develop deep learning projects. It is developed by Facebook AI Research lab in 2016 which has attracted data scientists’ and researchers attention since then due to its flexibility and wide applications. PyTorch uses python for programming language and also proved to be qualified for use in all kinds of professional settings. It has clear syntax, easy debugging and streamlined API which makes it a magnificent choice for implementing deep learning algorithms. Many deep learning enthusiast worldwide find it as a great tool for deep learning.
A tensor is known as the PyTorch’s core data structure which is a multidimensional array and also quite similar to arrays in numpy library. It is also capable of performing mathematical operations on an accelerated rate on dedicated hardware and thus allowing it to design and train neural network architectures more easier.
The simplicity of PyTorch makes it suitable for deep learning use cases as deep learning requires tools which are both flexible and can be adapt to a wide range of applications. It is quite easy to learn, use and debug and therefore used by many researchers and practitioners. It is written in python which allows developers of python to be more familiar with it. Moreover, PyTorch can be used to naturally program a deep learning machine and provides a data type, vectors, matrices or arrays and tensors to hold numbers.
PyTorch has two unique properties which makes it more suitable for deep learning. Firstly, it has accelerated computation using Graphical Processing Units (GPUs), which often gives rise to a speed which is 50 times more than same computation done on a CPU. In addition, it supports numerical optimization on generic mathematical expressions, which is used for training by deep learning models. Both of the properties mentioned above are equally beneficial for scientific computing as well.
PyTorch also allows a developer to implement models without too much complexity imposed by the library and allows a smooth transition of ideas into python code in the deep learning arena. This makes it a widely used library in research and also has there are higher number of citation counts for research papers which used PyTorch. A similar library known as TensorFlow has a robust pipeline for production purposes whereas, PyTorch is used more in research and teaching communities due to its ease of use.
The core PyTorch modules for building neural networks can be found in torch.nn which gives common neural network its layers and other architectural properties. Dense layers, convolutional layers, activation functions, and loss functions can all be found here. These components can be used to develop and initialize the untrained model. Moreover, due to tensors and auto-grad-enabled tensor standard library,, PyTorch can also be used for physics, rendering, optimization and modeling as well.
To learn more about PyTorch check the link below —
