5 brilliant Ways to Use PyTorch for AI Applications

What is the new update in PyTorch 1.1 and why should you and your team use it for your future AI applications? With the new release of PyTorch 1.1, it was noticed that Facebook has added a variety of brand new features to their popular deep learning library. In this support for TensorBoard, a suite of visualization tools that were originally created by Google for its deep learning library and TensorFlow are included.

PyTorch 1.1 also has an additional venefit of coming with an improved JIT compiler, which adds onto the PyTorch’s built-in capabilities for scripting. One of the greatest changes with this version 1.1 release is that it has the ability to perform distributed training on multifold GPUs, which allows the users to train extremely fast on very deep learning models.

There are courses online wherein you can learn how to implement a Convolutional Neural Network using Pytorch

In this blog, I will be delving into the new features of the latest PyTorch release, the exciting outcomes the technical the world has seen from it in just the course of three years, and also why you and your team members may be fascinated to make use of it in the future AI applications.

What exactly is PyTorch and what purpose does it resolve?

PyTorch is one of the deep learning libraries built for Python programming language by the Facebook AI Research team. Even now it is relatively new, with the original model released in October 2016 and version 1.1 releasing in spring 2019. PyTorch provides users with the ability to perform tensor computing, the fundamental base of deep learning. It also provides us with built-in automatic differentiation, which is how deep learning networks actually learn from the data sets.

PyTorch students also learn

PyTorch uses a very dynamic graph approach of computation, allowing its users to have access to literally every level of computation. This helps the developers to understand their code better and see what exactly is happening at each and every step in the code. Since the computational graph is defined at the runtime, this allows a direct integration with the Python’s built-in debugging tools.

Benefits of using PyTorch

1. Python-friendly. PyTorch was directly built with keeping Python in mind, unlike the other deep learning libraries that are ported over to Python. It also provides a hybrid front end enabling its users to seamlessly share majority of the code between the prototyping and the research phase and then directly move onto the graph execution mode for production.

Empower your team and lead the industry.

2. Optimized for GPUs supported by AWS and Azure. PyTorch has also been optimized to take advantage of GPUs for an accelerated training times. The largest of the cloud service providers are also on board with this particular development, as Amazon Web Services currently supports the latest version of PyTorch, which has been optimized for GPU and it even includes it in its Deep Learning AMI (Amazon Machine Image). Microsoft is also planning to support PyTorch in their Azure cloud offerings. It also has a built-in declarative data parallelism, allowing you as its users to leverage multiple GPUs on cloud providers.
3. Rich ecosystem of the tools and the libraries. PyTorch is also including several of the tools and libraries, with a rich ecosystem of tools as well as libraries for extending the PyTorch, this includes additions such as Torchvision, PyTorch’s built-in tools for working with the complex image datasets. The PyTorch ecosystem also includes projects, tools, models, and libraries from a broad community of the researchers in the academia and industry, application developers, and ML engineers. The goal this ecosystem has is to support the developers and data scientists in the exploration and the application of deep learning using PyTorch.

5 ways to make use of PyTorch for your AI applications

Using PyTorch for the deep learning tasks allows you as a user and your team to create predictive types of algorithms from data sets. For example, you could even leverage the historical real estate data to predict the future housing prices or even use a manufacturing plant’s historical production data to predict the failure rates on the new parts. Other common uses of PyTorch are:

• Image classification: PyTorch can be a tool that can be used to build specialized neural network architectures which are called Convolutional Neural Networks (CNNs). These multilayer CNNs are federal images of one specific thing, say, a kitten, and much like how the human brains works, once the CNN sees and processes a data set of kitten images, it should be able to confidently identify another image of a kitten. This application is seeing momentum in healthcare; there a CNN was recently used in one of the studies to detect skin cancer.
• Handwriting recognition: This application involves deciphering the human handwriting and its inconsistencies from one person to the other person and across different languages. Facebook’s Chief AI Scientist, Yann LeCun, pioneered CNNs in a way that it could recognize handwritten numerical digits.
• Forecast time sequences: A Recurrent Neural Network (RNN) is a type of neural network designed for the sequence modeling and is specifically useful for the purpose of training an algorithm on the past. It can make the decisions and predictions based on past data, so that it can make decisions based on the past. For instance, an airline may want to forecast a number of passengers it will have in a single month based on the data set generated from the past months.
• Text generation: RNNs and PyTorch also power the text generation, which is the training of an AI model on a particular text to create its very own output on what it has learned.

Which companies make use of PyTorch?

According to the market tracking done by HG Insights, companies including but not restricting: NVIDIA, Walmart, Apple, ADP and Pepsico, are making use of PyTorch to create models which make use of deep learning. Loads of thanks to these few major corporations’ adoption of this technology, the three major cloud providers—Amazon, Microsoft, and Google—are now providing cloud computing instances that have PyTorch 1.1 preinstalled and is ready to go right out of the box.

Thank you for reading this blog!!

I hope you have a wonderful rest of your day!!