🎯 Test Your Knowledge!​

1. What is the process of adjusting network weights to minimize the difference between predicted and actual outputs?

2. What is the basic computational unit of an artificial neural network?

3. What is the main goal of backpropagation?

4. Which function is most commonly used as an activation function in deep learning?

5. What is the main role of an "optimizer" (e.g., Adam, SGD) in network training?

6. In “deep learning,” what does the term “deep” refer to?

7. What function introduces non-linearity into a neuron's output, enabling complex pattern learning?

8. Which network is often used for unsupervised tasks like dimensionality reduction or learning features from its own input?

9. What defines a neural network's architecture or training process, set before training begins?

10. What determines the strength of the connection between two neurons in a neural network?

11. Which network architecture is best for generating new, realistic data such as human faces that don't actually exist?

12. What is the primary benefit of using "batch normalization" layers in deep neural networks?

13. Which specialized recurrent network handles long-term dependencies in sequential data like spoken words?

14. What does a "loss function" primarily measure during neural network training?

15. Which neural network architecture is primarily used for image recognition and classification tasks?

16. In a deep network, gradients become extremely small in early layers, hindering learning. What problem is this?

17. Randomly deactivating neurons during training to prevent overfitting is known as what regularization technique?

18. What is a Neural Network inspired by?

19. A network performs very well on training data but poorly on new, unseen data. What is this common problem called?

20. Which neural network type is best suited for processing sequential data like text or time series?