Deep-Learning-with-PyTorch-Tutorials is a companion repository for an introductory deep learning course built around PyTorch. It provides source code, notebooks, and presentation materials for a practical video-based learning path. The lessons begin with PyTorch setup, tensors, indexing, mathematical operations, gradients, and basic optimization. They then move into neural networks, logistic regression, multilayer perceptrons, CNNs, ResNet, RNNs, LSTMs, autoencoders, VAEs, GANs, graph convolutional networks, and transfer learning. The repository is designed for learners who want to connect deep learning concepts with executable examples. Overall, it is a structured PyTorch practice resource for beginners and early deep learning practitioners.
Features
- PyTorch deep learning tutorial materials
- Source code, notebooks, and presentation files
- Lessons from tensors and gradients to neural networks
- Coverage for CNNs, ResNet, RNNs, LSTMs, VAEs, GANs, and GCNs
- Setup guidance for Anaconda, CUDA, PyTorch, and common libraries
- Practical companion repository for a video course