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# PyTorch Tutorial for Deep Learning

**[JetBrains](https://daily.dev/sources/jetbrains)** · 27 min read · 0 upvotes · 0 comments

## Summary

A beginner-friendly guide to deep learning with PyTorch, covering tensors, neural network construction using torch.nn, and a complete hands-on project building an MNIST handwritten digit classifier. The tutorial walks through data loading and normalization, model definition with fully connected layers, training loops with CrossEntropyLoss and the Adam optimizer, and model evaluation achieving ~97% test accuracy. Setup instructions for PyCharm with uv or Conda environments are included, along with tips on using PyCharm Pro's Jupyter console and debugger to inspect tensors and step through training. Advanced topics like GPU acceleration, distributed training with DistributedDataParallel, and model deployment with TorchServe are briefly introduced as next steps.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://blog.jetbrains.com/pycharm/2026/07/pytorch-tutorial-for-deep-learning>

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Tags: [#python](https://daily.dev/tags/python), [#devtools](https://daily.dev/tags/devtools), [#deep-learning](https://daily.dev/tags/deep-learning), [#neural-networks](https://daily.dev/tags/neural-networks), [#pytorch](https://daily.dev/tags/pytorch)

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