What are some common beginner mistakes in deep learning?

What are some common beginner mistakes in deep learning?

Some common mistakes include not preprocessing data properly, overfitting models, and not understanding the architecture.

Can you explain overfitting and how to avoid it?

Overfitting occurs when a model learns the training data too well, including noise. To avoid it, use techniques like regularization, dropout, and cross-validation.

What preprocessing steps should I take before training a model?

Key preprocessing steps include normalizing data, handling missing values, and encoding categorical variables.

How important is choosing the right architecture?

Choosing the right architecture is crucial as it impacts the model's ability to learn patterns effectively.

What resources can I use to learn more about deep learning?

You can explore online courses, textbooks, and research papers. Websites like Coursera, edX, and ArXiv are great starting points.