What I Learned from Reading the PyCharm PyTorch Tutorial

I recently read the "PyTorch Tutorial for Deep Learning" on the PyCharm Blog by JetBrains as part of my AI learning journey.

Although this wasn't a hands-on coding session, it helped me build a stronger understanding of the fundamentals behind PyTorch and modern deep learning.

Highlights from the Tutorial

• An introduction to PyTorch and why it's one of the most popular deep learning frameworks.
• Understanding tensors and their role in neural networks.
• Creating, reshaping, and manipulating tensors.
• Basic tensor arithmetic and NumPy interoperability.
• Using CUDA for GPU acceleration.
• How torch.nn.Module is used to build neural networks.
• Understanding the purpose of the forward() method.
• Learning how Autograd automatically computes gradients during training.
• Setting up a PyTorch development environment in PyCharm.

Why This Article Stood Out

What I appreciated most is that the tutorial focuses on explaining why these concepts matter before jumping into building models. It provides a clear roadmap for beginners who want to understand the building blocks of deep learning.

What's Next?

My next step is to put these concepts into practice by building beginner-friendly PyTorch projects, starting with the MNIST handwritten digit classifier mentioned in the tutorial.

Learning is most valuable when theory is followed by practice, and that's exactly what I plan to do next.

If you're beginning your journey into AI or deep learning, this tutorial is a great place to start.

Original article: https://blog.jetbrains.com/pycharm/2026/07/pytorch-tutorial-for-deep-learning/

Happy learning! 🚀