An Introduction To PyTorch For Beginners

Introduction

PyTorch is an open-source machine learning library for Python and the fastest-growing Deep Learning framework. It is also a Python-based scientific computing library and it's generally used for applications such as natural language processing and its also help to achieve tensor computations with strong GPU acceleration support and building deep neural networks on tape-based autograd systems with the maximum flexibility.

At this time, there are many more existing libraries that have the potential to perform deep learning and artificial intelligence but one of the most important reasons behind PyTorch success in the real world is that it is completely Pythonic which means it allows smooth integration with the Python data science stack and it can leverage all the services and functionalities offered by the Python environment. As it's a dynamic library that means it's very flexible, we can use it as per our requirements and it also provides a platform that offers dynamic computational graphs and building a neural network model effortlessly. 

Some features of PyTorch

Easy Interface  
It offers an easy-to-use API which means we can easily integrate, operate, and runs on the Python ecosystem. The code execution in this framework is very easy and it uses python integrations coupled with a data science stack.

Computational Graphs 
It provides an outstanding platform that offers dynamic computational graphs and a user can change the graph using PyTorch even at runtime and this is extremely useful because when a user has no idea about how much memory is needed for generating a neural network model.

Cloud Support
It provides support for cloud platforms like providing frictionless development with large-scale training on GPUs, ability to run models in a production scale environment which increases the flexibility and productivity of the system.

Hybrid Front-End
It also provides a new hybrid front-end which means there are two modes of operation that is eager mode and graph mode. In which eager mode provides flexibility and ease of use and it is generally used for research in high performance and development. And on the other hand, graph mode is generally used for production because it provides better speed, optimization, and functionality in the C++ execution environment.

Conclusion

PyTorch is very easy to learn as compare to other deep learning frameworks because its syntax and application are similar to many conventional programming languages like Python, which helps us to deal with a complex problem and provide a solution in very little time which increases the productivity of the company.

Thanks