What are the key steps in data preprocessing for deep learning?

What are the key steps in data preprocessing for deep learning?

The key steps include data cleaning, normalization, transformation, and splitting into training and testing sets.

Can you explain data normalization and why it's important?

Data normalization scales the data to a standard range, improving model convergence and performance.

What techniques can I use for data cleaning?

Common techniques include handling missing values, removing duplicates, and correcting inconsistencies.

How do I split my dataset into training and testing sets?

You can use a simple random split, typically 70-80% for training and 20-30% for testing.