PyTorch is an open source machine learning (ML) framework based on the Python programming language and the Torch library. Torch is an open source ML library used for creating deep neural networks and is written in the Lua scripting language. It’s one of the preferred platforms for deep learning research. The framework is built to speed up the process between research prototyping and deployment.
The PyTorch framework supports over 200 different mathematical operations. PyTorch’s popularity continues to rise, as it simplifies the creation of artificial neural network models. PyTorch is mainly used by data scientists for research and artificial intelligence (AI) applications. PyTorch is released under a modified BSD license.
PyTorch was initially an internship project for Adam Paszke, who at the time was a student of Soumith Chintala, one of the developers of Torch. Paszke and several others worked with developers from different universities and companies to test PyTorch. Chintala currently works as a researcher at Meta — formerly Facebook — which uses PyTorch as its underlying platform for driving all AI workloads.
PyTorch is pythonic in nature, which means it follows the coding style that uses Python’s unique features to write readable code. Python is also popular for its use of dynamic computation graphs. It enables developers, scientists and neural network debuggers to run and test a portion of code in real time instead of waiting for the entire program to be written.
PyTorch provides the following key features:
Using PyTorch can provide the following benefits:
PyTorch is often compared to TensorFlow, a deep machine learning framework developed by Google. Because TensorFlow has been around longer, it has a larger community of developers and more documentation.
However, PyTorch does have advantages over TensorFlow. PyTorch dynamically defines computational graphs, unlike the static approach of TensorFlow. Dynamic graphs can be manipulated in real time. Additionally, TensorFlow has a steeper learning curve, as PyTorch is based on intuitive Python.
TensorFlow may be better suited for projects that require production models and scalability, as it was created with the intention of being production ready. However, PyTorch is easier and lighter to work with, making it a good option for creating prototypes quickly and conducting research.
PyTorch is one of the most popular deep learning frameworks due to its flexibility and computation power. It’s easy to learn and is used in many applications, including natural language processing (NLP) and image classification.
The following are a few common use cases of PyTorch:
NLP. NLP is a behavioral technology that enables a computer to understand human language as it’s spoken or written. Main elements of NLP include machine translation, information retrieval, sentiment analysis, information extraction and question answering.
Deep neural networks are behind several breakthroughs in machine understanding of natural languages such as Siri and Google Translate. But most of these models employ a recurrent neural network method to treat language as a flat sequence of words, whereas many linguists support the recursive neural network model, as they believe that language is best understood when presented in a hierarchical tree of phrases. PyTorch makes these complicated language models easier to understand. For example, in 2018, Salesforce developed a multi-task NLP learning model that performs 10 tasks at once.
Reinforcement learning. The Python library known as Pyqlearning is used for executing reinforcement learning (RL), which is a subset of ML. In RL, a machine is made to learn from experience so that it can take proper decisions to get the best reward possible. RL is mainly used for developing robotics for automation, robot motion control or business strategy planning, and employs the Python Deep Q learning architecture for building a model.
Image classification. This process classifies an image based on its visual content by using an image classification algorithm. For example, the algorithm can tell a computer vision application whether a certain image contains a cat or a dog. While object detection is effortless for the human eye, it can be challenging for computer vision applications. By using PyTorch, a developer can process images and videos to create an accurate computer vision model.
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