This course teaches you deep learning the practical way—by building real neural networks step-by-step in PyTorch. You start with the basics of tensors, autograd, and the PyTorch training pipeline, then move into building and training your own models from scratch.

You’ll work with the nn module, create custom datasets and dataloaders, train on GPU, and learn how to optimize and tune your networks for better performance. The course also covers advanced workflows, including hyperparameter tuning with Optuna, transfer learning, CNNs for vision tasks, and RNN/LSTM models for text-based applications like next-word prediction and question answering.

By the end, you’ll know how to take an idea, turn it into a working deep learning model, train it efficiently, and evaluate it with industry-ready techniques—all using PyTorch.

What you will learn:

• Core PyTorch concepts: tensors, autograd, modules
• Building and training neural networks end-to-end
• Managing datasets and dataloaders
• GPU-based model training
• Network optimization and debugging
• Hyperparameter tuning with Optuna
• Building CNNs for computer vision
• Transfer learning for faster, high-accuracy models
• Implementing RNN/LSTM models for NLP tasks

This course is designed for learners who want hands-on experience and a strong foundation for real-world deep learning projects.