Which is better for machine learning: TensorFlow or Scikit-Learn?

I'm trying to decide between TensorFlow and Scikit-Learn for my machine learning project. Which one should I choose?

It depends on your project requirements. TensorFlow is great for deep learning and large datasets, while Scikit-Learn is more suited for traditional machine learning tasks.

What about the learning curves for both? How do they compare?

TensorFlow's learning curve can be steeper due to its complexity, but it offers more flexibility. Scikit-Learn is easier to learn and use for beginners.

Can you give me examples of projects where each would be more appropriate?

Sure! Use TensorFlow for image recognition or natural language processing, and Scikit-Learn for tasks like regression or classification with smaller datasets.

Are there any performance differences I should be aware of?

Yes, TensorFlow generally performs better on large-scale problems, while Scikit-Learn is efficient for smaller datasets and simpler models.