The open-source machine learning library PyTorch is based on Python’s Torch library and was developed and released in 2016 by AI Research Lab of Facebook. This free, open-source library is especially useful in the following context:
- Deep learning
- Computer vision
- NLP – Natural language processing
Pytorch uses tensor and NumPy multi-dimensional arrays to let developers create complex neural networks. PyTorch is being increasingly adopted in industries and in research due to its speed, flexibility, and the ease with which you can get Pytorch up and running. All these factors combine to make Deep learning with PyTorch such an excellent choice.
Top Use Cases
PyTorch particularly shines in any of the following use cases:
Natural language processing
Top Reasons to Choose PyTorch
There are several reasons that serve to make PyTorch a leading machine learning library choice among developers and researchers. They are:
It is based on Python
As per experts, Python is one of the fastest growing programming languages. In the last decade, the community surrounding Python has grown rapidly. In fact, Python is also a leading choice for both industrial and academic AI and ML development projects. PyTorch is based on Python making sure that developers who are well acquainted with the latter feel right at home while coding with PyTorch. Additionally, PyTorch comes with a C++ frontend as well adding to its already considerable usability. While using PyTorch developers can use popular Python packages like SciPy, NumPy and Cython which add to the core functionality of Python.
PyTorch is Easy to Learn
Like Python, PyTorch too stands out by the ease with which you can learn the language. While it is perhaps not child’s play but compared to other deep learning libraries, frameworks, and tools, PyTorch is relatively easy to grasp primarily due to the syntax which is intuitive and easy.
Vibrant and Active Community Surrounding the Library
In the relatively short span of time that it has been around, PyTorch has managed create a dedicated developer community. In addition to the strong, active and vibrant developer community PyTorch is known for its organized and comprehensive documentation which aids the deep learning with pytorch development.
Debugging is quite easy
The tight integration between Python and PyTorch ensures that you can use debugging tools meant for the Python language work with PyTorch as well. You can for example use Python debugging tools like ipdb and pdb to debug the code you create on PyTorch. You can even use the PyCharm debugger with PyTorch for the same debugging purposes.
Machine Learning with PyTorch
Deep learning with PyTorch stands by the extensive flexibility it offers and the speed with which you can implement deep neural networks using it.
PyTorch stands out from other ML tools like TensorFlow in the dynamic nature of its computational graphs. In contrast to static computational graphs used by other ML technologies that are defined before runtime, dynamic computational graphs are defined in real-time by using forward computation. To put it differently the graphs are created from ground up at every iteration.
Pytorch and Artificial Intelligence
PyTorch is a leading ML framework and researchers from top universities and academic institutions like Carnegie Mellon and Stanford use it. The ease of use and flexibility of the library largely accounts for its popularity both in the industry and the academia. It is a smart choice for people wanting to create deep learning models. Further leading cloud computing services like Amazon AWS and Microsoft Azure provide specialized services that make it easy for developers to deploy the deep learning models on their platforms.
PyTorch on AWS
AWS goes to the extent of providing a special, dedicated Amazon S3 plugin for PyTorch. The plugin provides the dataset library of PyTorch with a performance boost. It eases the task of data access where the data is stored in S3 buckets. Further the plugin makes it possible to access streaming data irrespective of the data size. In other words that means that developers need no longer create provision for local storage of data. The plugin lets you use the high throughputs that Amazon S3 offers while keeping latency levels to a bare minimum.
PyTorch on Azure Cloud
The Azure Machine Learning solution lets developers scale out PyTorch model training tasks with the elastic cloud computing resources it offers. It does not matter if you want to train a model from scratch or if you want to migrate an existing model or PyTorch view into the Azure cloud platform. With Azure, PyTorch developers can create, deploy, manage versions and monitor deep learning models ready for production use!
PyTorch Top Use case
PyTorch lets developers create predictive algorithms based on differing datasets through deep learning models. Some of the common use cases of PyTorch are as follows:
PyTorch lets developers create Convolutional Neural Networks (CNN) which are a type of specific neural network architecture. PyTorch CNN’s have multiple layers and can take several images of objects. With that input, the system works much like the human brain and the model can then continue to identify other images of the same kind of object. CNNs are presently used in advanced healthcare institutions to identify ailments like skin cancer.
PyTorch based RNNs can be used to create AI models capable of generating text based on specific texts. After due training, the AI model can create text following the patterns it has learnt from the training data which in this case is a specific text.
PyTorch models can analyse human handwriting taking into consideration the inconsistencies that occur based on the concerned individual and language. Meta has become a pioneer for creating CNNs that can perceive numbers written by the human hand
Forecasting time sequences
PyTorch also lets developers create and work with recurrent neural networks (RNNs). This is a specific kind of neural algorithm that provides people with crucial information on the basis of past data. RNNs can make correct predictions about things like number of passengers in a particular month for an airline based on previous data.
Transfer of style is one of the most amusing, funny, and popular application of the PyTorch model. In such applications, the deep learning algorithm can change videos or images to base it on another video or image.
PyTorch Solution Expertise:
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