Скачать или смотреть PyTorch Tutorial for Beginners (FULL COURSE) – Learn Deep Learning Step-by-Step

Learn PyTorch from ZERO to building fully-working neural networks in this complete beginner-friendly tutorial!
In this full course, you’ll understand everything from tensors and operations to autograd, training loops, activation functions, loss functions, DataLoaders, and saving/loading models—step-by-step and with clear explanations.

Whether you want to build AI models, understand deep learning, or prepare for a machine learning job, this tutorial gives you all the foundations you need.

✨ What You Will Learn

✔ Installing PyTorch & setting up GPU
✔ Understanding tensors & tensor operations
✔ Broadcasting, matrix multiplications, reshaping
✔ How autograd works (automatic differentiation)
✔ Building neural networks with nn.Module and nn.Sequential
✔ Activation functions (ReLU, Sigmoid, Tanh)
✔ CrossEntropyLoss vs manual softmax
✔ Full training loop (forward → loss → backward → optimize)
✔ Evaluating model performance
✔ Saving & loading PyTorch models
✔ Using DataLoaders for real-world datasets

🧠 Code Topics Covered

Creating tensors, random initialization
Dot products, matrix multiplications, shape rules
Forward pass & backprop using autograd
Adam optimizer, MSELoss, CrossEntropyLoss
GPU (CUDA) acceleration
Mini-batch training with DataLoader

Github Code Link: https://github.com/ysimokat/PyTorch

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