I’m new to deep learning and currently training my first few neural network models. During training, the accuracy keeps improving and the loss goes down nicely. But when I evaluate the model on validation data, the performance drops a lot. This feels confusing because the training results look “good” at first glance. I’m trying to(Read More)
I’m new to deep learning and currently training my first few neural network models.
During training, the accuracy keeps improving and the loss goes down nicely. But when I evaluate the model on validation data, the performance drops a lot. This feels confusing because the training results look “good” at first glance.
I’m trying to understand this at a conceptual level, not just apply fixes blindly.
Some things I’m wondering about:
- What are the most common reasons this happens for beginners?
- How do you tell if this is overfitting versus a data or setup issue?
- Are there simple checks or habits I should build early to avoid this?
- At what point should I worry, and when is this just part of learning?
Looking for intuition, mental models, and beginner-friendly explanations rather than advanced math or theory.
