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What is PyTorch? A complete guide for beginners covering features, use cases, and installation
Overview and history of PyTorch
PyTorch is an open-source machine learning library developed by Facebook (now Meta). Built on Python, its high flexibility and ease of use have led to widespread adoption across many fields, from research and development to practical industrial applications. It is especially popular as a deep learning framework and is often mentioned alongside TensorFlow. PyTorch’s development began when Facebook’s AI research team, inspired by another framework called Chainer, set out to create a tool better suited for flexible, experimental research. As a result, PyTorch established itself as a powerful platform for researchers to quickly try out new ideas. More than just a way to write code, PyTorch is supported by many developers and researchers as a tool for exploring the cutting edge of AI technology.
Key features of PyTorch
PyTorch’s biggest feature is its support for dynamic computation graphs. This means the computation graph can be flexibly modified while the program is running. This makes it easy to develop and debug complex neural network models. PyTorch also provides an intuitive, easy-to-use API, so even beginners in machine learning can get started relatively easily. In addition, it offers high flexibility, allowing researchers to make the most of its freedom when implementing new architectures and algorithms. Taken together, these features have established PyTorch as a powerful tool for accelerating AI research and development. You could say PyTorch goes beyond a mere library to serve as a vital foundation for shaping the future of AI.Comparison with other frameworks
Compared with TensorFlow, a flagship deep learning framework, PyTorch is better suited to experimental research and development, standing out for its dynamic computation graphs and ease of debugging. TensorFlow excels in large-scale production environments, whereas PyTorch is often preferred in research settings because it enables more flexible, rapid prototyping. Thanks to its intuitive API and strong affinity with Python, PyTorch is also considered easy to learn for beginners just starting with machine learning. Meanwhile, TensorFlow has different strengths, being optimized for larger-scale deployments and mobile environments. Which framework to choose should depend on your project’s specific requirements, your development team’s experience, and your ultimate goals. Both frameworks have active communities that provide abundant information and support, so you can use either with confidence.
Installing PyTorch and Setting Up Your Environment
System Requirements and Preparation
Before installing PyTorch, you need to have Python installed. You’ll also need pip, Python’s package management tool. Make sure these tools are installed and update them to the latest versions if needed. In addition, to accelerate deep learning computations, it’s recommended to use a CUDA-capable GPU and install the corresponding CUDA drivers and the cuDNN library. However, these are not mandatory; you can run computations on the CPU only. If you use a GPU, you’ll need to download and install the appropriate drivers and libraries from the NVIDIA’s official website. Installation involves several steps and caveats, so be sure to read the official documentation before proceeding.オープンソースの力を活用する方法～Ubuntuの世界へようこそ～
目次 1 1. はじめに2 2. 前提条件2.1 2.1 対応GPUの確認方法2.2 2.2 Ubuntuのバージョン確…
How to Install PyTorch
The easiest way to install PyTorch is to use the install command provided on the official website. On the site, select the command that matches your environment (OS, Python version, whether CUDA is available, etc.), then run it in your terminal or command prompt to complete the installation. If you use a package manager such as conda, you can also install PyTorch with conda commands. During installation, you can choose between the CPU and GPU builds, so pick the appropriate version for your setup. After installation, try importing PyTorch to confirm it was installed correctly. If you encounter an error, read the message carefully and look for solutions in the official documentation or community forums.Using Google Colab
Google Colab is a very convenient cloud environment where you can run Python code in your browser. PyTorch comes preinstalled, and you can use GPUs for free, making it ideal if setting up a local environment is difficult or if you want to try deep learning quickly. By using Google Colab, you can skip the hassle of environment setup and start programming with PyTorch right away. In addition, Google Colab makes it easy to share code and collaborate, so it’s useful for team development and in educational settings. However, Google Colab may disconnect your session after a period of inactivity, so be careful when running long computations. Also, the GPUs available in Google Colab are limited, so training large models may require a more powerful environment.
Basic Operations in PyTorch
What is a tensor?
The core data structure in PyTorch is the tensor. A tensor represents the mathematical concept of a multi-dimensional array and is used to store numerical and multi-dimensional data. Specifically, you can work with vectors, matrices, and even higher-dimensional data structures. Tensors in PyTorch support operations very similar to those of NumPy arrays and play a central role in various computations in numerical computing and deep learning. A tensor has attributes such as shape, data type, and device (CPU or GPU), and based on this information, computations are executed efficiently. PyTorch provides many functions for flexible tensor manipulation, and by leveraging them you can build complex AI models.Creating and manipulating tensors
In PyTorch, you can easily create tensors from lists or NumPy arrays using the torch.tensor() function. You can also generate tensors filled with zeros or ones of a specified shape using functions like torch.zeros() and torch.ones(). For tensors you create, you can change their shape with reshape(), perform basic arithmetic such as addition, subtraction, multiplication, and division, and use slicing and indexing to extract specific parts. These operations can be carried out in much the same way as with NumPy arrays. In addition, PyTorch offers a rich set of functions for working with tensors, including type conversions, data transfer between devices, and various math functions. Mastering these functions will help you develop AI with PyTorch more efficiently. Because tensor operations are among the most important elements of working effectively with PyTorch, it’s crucial to understand them well.Building neural networks
When building a neural network in PyTorch, you create your own model class by subclassing nn.Module. Inside this class, you define the required layers (linear layers, convolutional layers, pooling layers, etc.) and specify the data flow in the forward() method. This allows you to flexibly construct many different types of neural network models. PyTorch comes with common layers and activation functions out of the box, and by combining them you can easily implement complex models. You can also define your own custom layers and activation functions, which is extremely useful in research and development. After defining the model, set up the loss function and optimization algorithm and train it. PyTorch provides various tools to streamline the training process, and by leveraging them you can build more accurate models efficiently.AI development with PyTorch
Building image recognition AI models
PyTorch is widely used as a powerful tool in image recognition. In particular, its ease of implementing convolutional neural networks (CNNs) makes it suitable for a wide range of tasks, including image classification, object detection, and semantic segmentation. PyTorch comes with well-known CNN models such as ResNet, VGG, and Inception predefined, allowing you to easily build your own models on top of them. It also makes it easy to train on large datasets such as ImageNet, enabling rapid development of high-performance image recognition models. Thanks to PyTorch’s flexibility and ease of use, it is widely used not only in research and development but also in industry. For developing image recognition AI models, PyTorch offers a wealth of tools to efficiently handle the entire workflow—data preprocessing, model training, and evaluation—and by leveraging these, you can accelerate the development of more advanced image recognition models.Applications in natural language processing
PyTorch also plays a crucial role in natural language processing (NLP), thanks to its flexibility and expressive power. State-of-the-art models, including Transformer models, can be implemented relatively easily with PyTorch and are used for various NLP tasks such as text classification, machine translation, text generation, and question answering. Large language models like BERT and GPT are also implemented in PyTorch and widely used. By training on massive amounts of text data, these large models can generate text comparable to human writing and understand complex linguistic nuances. PyTorch provides a variety of tools that make it easier to develop and use these large models, greatly contributing to advances in AI technology within NLP. PyTorch also offers robust libraries for efficiently performing data preprocessing, model training, and evaluation for NLP tasks, enabling developers to quickly prototype and run experiments.Real-world use cases
Thanks to its versatility, PyTorch is being applied in practical ways across a wide range of fields, including healthcare, finance, manufacturing, and entertainment. In healthcare, it helps with early disease detection and diagnostic support in medical image analysis (X-ray, CT, MRI, etc.). In finance, it is used for stock price prediction, risk management, and fraud detection, supporting more efficient and secure financial transactions. In manufacturing, it is used for defect detection and quality control, contributing to higher productivity. In entertainment, techniques such as image and video generation, speech recognition, and natural language processing are used to develop new content and services. As these examples show, PyTorch serves as a key technological foundation for creating innovative solutions across many fields, not just in specific domains. Looking ahead, PyTorch use cases are expected to expand even further, bringing major transformations to our lives and society.
Level up your PyTorch learning
Leverage the official documentation and community
PyTorch’s official documentation is extremely comprehensive, offering abundant guidance on API usage, detailed explanations of various features, and practical examples. By carefully reading the official docs, you can learn PyTorch systematically from the basics to advanced applications. PyTorch also has a very active community where developers and researchers worldwide exchange information and ask questions. By participating in community forums, you can resolve your doubts and learn from other developers’ experiences. In addition, GitHub hosts many open-source projects built with PyTorch, and reading their code will help you gain more practical knowledge. Because the official documentation and community are crucial resources for learning PyTorch, make sure to use them actively.
Learn through online courses and books
To learn PyTorch systematically, it’s also effective to take courses offered on online education platforms like Udemy, Coursera, and edX, as well as use books available for purchase. Online courses let you learn PyTorch from the basics to advanced topics through videos and hands-on formats, while books help you gain a deeper understanding of the theoretical background and practical techniques. You can choose these materials to match your learning style and level, making them very useful for self-study. Some courses and books also include exercises based on real projects and practical code examples, and using these resources will help you build more hands-on skills. Online courses and books are powerful tools for learning PyTorch efficiently.Hands-on practice
When learning PyTorch, it’s crucial not only to study the theory but also to actually write and run code. Start with simple tasks and step up gradually to deepen your understanding of PyTorch. For example, try implementing a simple linear regression or classification model, or train using publicly available datasets. Participating in data analysis competitions such as Kaggle is also very effective for sharpening practical skills. By joining competitions, you can not only gauge your skill level but also learn from other participants’ code and techniques. Hands-on practice is indispensable not only for solidifying your PyTorch knowledge but also for laying the foundation for more advanced AI development. By actively getting hands-on and tackling various challenges, you’ll be able to master PyTorch effectively.
