This comprehensive PyTorch course takes learners from beginner to advanced levels, providing a complete roadmap for deep learning and machine learning with practical examples. Starting with environment setup and installation, the course introduces tensors, their operations, and PyTorch’s autograd system for automatic differentiation. You will understand backpropagation, gradient descent, and the training pipeline including model, loss function, and optimizer configuration.

The course covers linear and logistic regression, feed-forward neural networks, convolutional neural networks (CNNs), transfer learning, and recurrent architectures like RNNs, LSTMs, and GRUs. Learners gain experience handling datasets, DataLoaders, transformations, and batch training.

Advanced topics include model performance optimization, learning rate scheduling, PyTorch Lightning for streamlined research workflows, and distributed & multi-GPU training using DDP, FSDP, and Torchrun. Deployment concepts are covered with model saving/loading, monitoring with TensorBoard, and serving models in real-world applications via Flask, Heroku, and mobile runtimes.

By the end of this course, learners will be equipped to design, train, and deploy PyTorch models across various domains including computer vision, natural language processing, and generative AI.