PyTorch Tutorial for Deep Learning - The JetBrains Blog

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Data Science<br>PyTorch Tutorial for Deep Learning

Evgenia Verbina

This is a guest post from Naa Ashiorkor, a data scientist and tech community builder.

Building intelligent systems that can see, hear, understand language, and make decisions was previously the domain of specialized researchers with massive computing resources only – today, deep learning has made this accessible to developers and data scientists across the world, bringing the ability to build, train, and deploy AI models within reach.

This accessibility can be credited to deep learning frameworks, and one such framework is PyTorch, which has rapidly become the prevailing choice across both research and industry. PyTorch is an open-source deep learning framework built in Python and designed to make building neural networks intuitive.

Curious about how neural networks actually learn? In this tutorial, you’ll build your first PyTorch model using the MNIST dataset in PyCharm and see it recognize handwritten digits in real time. Along the way, you’ll get familiar with tensors and understand the core workflow behind building deep learning models.

What is PyTorch?

PyTorch traces its roots to Torch, a scientific computing framework that used Lua; in 2016, researchers at Facebook’s AI Research Lab (FAIR), now Meta AI, reinvented it for Python, creating PyTorch, which is today a Linux Foundation community project.

By 2024, PyTorch had established itself as the most popular deep learning framework, with a 63% adoption rate in the model training space, used in over 70% of AI research implementations. In 2025, the PyTorch Foundation’s ecosystem grew to include large-scale projects such as vLLM, DeepSpeed, and Ray, all of which are governed independently.

The annual PyTorch Conference attracted more than 3,400 attendees and gained 16 new industry members, including Snowflake, Dell Technologies, and Qualcomm. Also, it is trusted in production by organizations such as Meta, Microsoft, OpenAI, and Tesla. For developers and data scientists looking to enter deep learning, PyTorch remains the most practical and widely supported starting point available today.

PyTorch was built on two foundations: GPU-accelerated tensor computation as a more powerful alternative to NumPy and an automatic differentiation engine for training neural networks.

From these foundations, PyTorch has grown into one of the most fully featured deep learning frameworks available. Its core features include:

Dynamic computation graphs (define-by-run): As code executes, PyTorch builds computation graphs. These are maps of every mathematical operation your model performs: things like multiplying inputs by weights, adding biases, and applying activation functions. PyTorch needs to track these because training requires working backwards through all of those steps to calculate how much each weight contributed to the model’s error, so it knows how to adjust them to improve. Computation graphs allow the model structure to be modified during runtime and facilitate debugging using standard Python tools, making PyTorch ideal for research and experimentation.

Pythonic and intuitive interface: PyTorch code is Pythonic, which reduces the learning curve. It uses standard Python control flow and clean, readable syntax, and it integrates well with Pythonic libraries.

Strong GPU acceleration: PyTorch has seamless support for GPUs using CUDA. There is easy device switching and efficient tensor computations on GPUs. It also supports multi-GPU training.

Autograd (automatic differentiation): There is a built-in autograd engine that automatically computes gradients. It tracks operations on tensors and enables backpropagation with minimal code.

Rich neural network library: PyTorch provides a comprehensive module for building models. There are prebuilt layers, loss functions, activation functions, and a modular design for custom architectures.

Extensive ecosystem: PyTorch is not just a framework – it is an ecosystem. There is a wide array of tools, even beyond the AI-specific libraries. Hence, an entire AI project can be managed under the Python umbrella from data collection to deployment.

Model deployment support: PyTorch supports deploying models from research to production, with support for both mobile and edge deployments. What’s more, it also has TorchScript for optimized execution and ONNX export for interoperability.

Broad community and industry adoption: PyTorch is backed by Meta, and it has a large and active community. Due to Python being one of the largest programming communities worldwide, PyTorch users benefit from shared knowledge, resources, and tools. There is extensive documentation and tutorials, and it is widely used in academia and industry.

For a broader perspective on how PyTorch and TensorFlow differ, and when to choose...