PyTorch is one of the fastest-growing Python-based frameworks for deep learning. Let’s have a look at the basics and how to build and deploy a model using Machine Learning. A practical walkthrough on getting started with PyTorch. Let’s look at the benefits of using ML project and a quick comparison between PyTorch and NumPy. Getting Started with PyTorch – Deep Learning in Python
Are you trying to design a model using machine learning?
If yes, PyTorch will be the right choice in that case. This article will help you understand the basics of deep learning and the concept of PyTorch. In the beginning, we will explain what PyTorch is & the advantages of using it for your projects. The article will end with a quick comparison between PyTorch and NumPy using an example.
Launched by Facebook back in 2016, PyTorch is an open-source machine learning framework. PyTorch belongs to the Torch library, and the primary intent behind the development of the framework is to facilitate the high-speed implementation of the neural networks.
What makes PyTorch a better framework for the creation and development of neural networks is the fact that it uses dynamic computational graphs. Unlike the other deep learning frameworks with static graphs, the dynamic ones are created on the fly, which means that the graph is computed after every step and on each iteration.
But that's not the only thing that accounts for the widespread usage of PyTorch. Here we have listed additional advantages offered by PyTorch.
It is a well-known fact that Python is one of the hottest programming languages of the decade. From machine learning to Artificial Intelligence, everything is coded using Python. PyTorch, for that matter, is Pythonic in nature. Python developers can easily understand and work on the PyTorch framework. This makes it a popular framework as compared to other deep learning frameworks.
Even though you aren't versed in Python language, learning PyTorch is pretty easy and wouldn't worry you much. The syntax is comparatively simple, and the overall framework is intuitive.
Being integrated with Python, PyTorch provides the flexibility to extend the usage of Python's debugging tools. In fact, all of the debugging tools rendered by Python can be used to debug programs in PyTorch.
In addition to all of the above, PyTorch is backed by a huge community of developers and programmers. Also, it has well-organized and structured documentation, which further makes it easier to create ML models using the framework.
Besides PyTorch, NumPy is another frequently used framework that helps in the creation of networks. To understand their difference, let's start with the creation of the network, first with NumPy and then with PyTorch.
PyTorch for Deep Learning | Data Science | Machine Learning | Python. PyTorch is a library in Python which provides tools to build deep learning models. What python does for programming PyTorch does for deep learning. Python is a very flexible language for programming and just like python, the PyTorch library provides flexible tools for deep learning.
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