PyTorch From Scratch | Train–Validation Split, Overfitting & Loss Functions | Mini-Test Solved
In this lecture, we cover the most important concepts required to train and evaluate
machine learning models correctly in PyTorch.
📚 Topics Covered:
• Train–Validation split and why it is necessary
• Overfitting vs Generalization
• Training loss vs Validation loss
• model.train() vs model.eval()
• torch.no_grad() and why it is required
• Choosing correct output layers and loss functions
• Regression vs Binary vs Multi-class classification
• Lesson 16 Mini-Test – Questions with complete solutions
🧠 This lecture focuses on understanding why things are done, not just *how*.
This video is part of the *PyTorch From Scratch* series and is useful for:
• Beginners learning PyTorch properly
• Students preparing for ML / DL internships
• Interview preparation (common pitfalls explained)
▶ Previous lectures covered Dataset, DataLoader, training loops, optimizers.
▶ Next lectures will move into CNNs and deep learning architectures.
📌 Playlist: PyTorch From Scratch Series