Why Use PyTorch with the Data Analysis?


PyTorch is an open-source machine learning framework based on the Torch library, used for applications such as computer vision and natural language processing, primarily developed by Facebook’s AI Research lab. It is free and open-source software released under the Modified BSD license.

As we mentioned above, PyTorch is one of the newest libraries out there that works with Python and can help with deep learning. Even though it was just released in January 2016, it has become one of the go-to libraries that data scientists like to work with, mainly because it can make it easy to build up really complex neural networks. This is perfect for a lot of beginners who haven’t been able to work with these neural networks at all in the past. They can work with PyTorch and make their own network in no time at all, even with a limited amount of coding experience.

The creators of this Python library envisioned that this library would be imperative when they wanted to run a lot of numerical computations as quickly as possible. This is one of the ideal methodologies that also fits in perfectly with the programming style that we see with Python. This library, along with the Python library, allowed debuggers of neural networks, machine learning developers, and deep learning scientists to not only run but also to test, parts of their code in real-time. This is great news because it means that these professionals no longer have to wait for the entire code to complete and execute before they can check out whether this code works or if they need to fix certain parts.

In addition to some of the functionality that comes with the PyTorch library, remember that you are able to extend out some of the functionalities of this library by adding in other Python packages. Python packages like Cython, SciPy, and NumPy all work well with PyTorch as well.

Even with these benefits, we still may have some questions about why the PyTorch library is so special, and why we may want to use this when it is time to build up the needed models for deep learning. The answer with this is simple, mainly that PyTorch is going to be seen as a dynamic library. This means that the library is really flexible and you can use it with any requirements and changes that you would like. It is so good at doing this job that it is being used by developers in artificial intelligence, students, and researchers in many industries. In fact, in a Kaggle competition, this library was used by almost all of the individuals who finished in the top ten.

While there are a lot of benefits that can come with the PyTorch library, we need to start off with some of the highlights of why professionals of all sorts love this language so much. Some of these include:

  1. The interface is simple to use. The PyTorch interface is going to offer us an API that is easy to use. This means that we will find it simple to operate and run as we do with Python.
  2. It is Pythonic in nature. This library, since it is considered Pythonic, will smoothly integrate to work with the Python data science stack. Those who do not want to work with other coding languages along the way, and want to just stick with the basics, and some of the power, of Python, will be able to do so with this library. You will get the leverage of using all of the functionalities and services that are offered through the environment of Python.
  3. Computational graphs: Another highlight that comes with the PyTorch library is that it is going to provide us with the platform with some dynamic computational graphs. This means that you have the ability to change these graphs up even during runtime. This is going to be useful any time that you need to work on some graphs and you are not sure how much memory you need to use while creating this model for a neural network.

Why Use PyTorch for Data Analysis:

Anyone who is working with the field of data science, data analysis, artificial intelligence, or deep learning has likely spent some time working with the TensorFlow library that we also talked about in this guidebook. TensorFlow may be the most popular library from Google, but because of the PyTorch framework for deep learning, we can find that this library is able to solve a few new problems when it comes to research work that these professionals want to fix.

It is often believed that PyTorch is now the biggest competitor out there to TensorFlow when it comes to handling data, and it is going to be one of the best and most favored artificial intelligence and deep learning library when it comes to the community of research. There are a number of reasons for this happening, and we will talk about some of these below:

First, we will notice that dynamic computational graphs are going to be popular among researchers. This library is going to avoid some of the static graphs that can be used in other frameworks from TensorFlow. This allows researchers and developers to change up how the network is going to behave at the last minute. Some of those who are adopting this library will like it because these graphs are more intuitive to learn when we compare them to what TensorFlow is able to do.

The second benefit is that this one comes with a different kind of back-end support. PyTorch is going to use a different backend based on what you are doing. The GPU, CPU, and other functional features will all come with a different backend rather than focusing on just one backend to handle all of these. For example, we are going to see the THC for our GPU, and TH for CPU. Being able to use separate backends can make it easier for us to deploy this library across a variety of constrained systems.

The imperative style is another benefit of working with this kind of library. This means that when we work with this library, it is easy to use and very intuitive. When you execute a line of code, it is going to get executed just as you want, and you are able to work with some tracks that are in real-time.

This allows the programmer to keep track of how the models for neural networks are doing. Because of the excellent architecture that comes with this, and the lean and fast approach, it has been able to increase some of the overall adoptions that we are seeing with this library throughout the communities of programmers.

Another benefit that we are going to enjoy when it comes to working with PyTorch is that it is easy to extend. This library, in particular, is integrated to work well with the code for C++ and it is going to share a bit of the backend with this language when we work on our own framework for deep learning. This means that a programmer is going to be able to not just use Python for the CPU and GPU, but can also add in the extension of the API using the C or C++ languages. This means that we are able to extend the usage of PyTorch for some of the new and experimental cases, which can make it even better when we want to do some research with it.

And finally, the last benefit that we are going to focus on here is that PyTorch is going to be seen as a Python approach library. This is because the library is a native Python package just by the way that it is designed. The functionalities that come with this are built as classes in Python, which means that all of the code that you write here can integrate in a seamless manner with the modules and packages of Python. Related article: Keras Library for Deep Learning

Similar to what we see with NumPy, this Python-based library is going to enable us to work on a tensor that is GPU-accelerated, with computations that provide us with some rich options for APIs while applying a neural network.

PyTorch is going to provide us with the research framework that we need from one end to another, which will come with most of the different building blocks that we need to carry out the research of deep learning on a day-to-day basis. And we also notice that this library is going to provide us with a high-level neural network module because it can work with an API that is similar to the Keras library as well.


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