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4. The number of hidden layers in a neural network can influence its ability to find more complex patterns.
6. An algorithm used in linear regression to adjust parameters to minimize the difference between observed and predicted values.
8. The specific mathematical operation performed by each neuron in a neural network to combine input values.
9. The mathematical function used to measure the difference between the predicted output and the actual output in a neural network.
12. The part of a neural network that receives input data represented as numbers.
14. A large-scale dataset used for training and testing various machine learning algorithms, including neural networks.
15. A program that imitates one neuron.
16. The type of data used to train a neural network, consisting of input-output pairs.

1. A danger in training neural networks where the model fits too closely to the training data, resulting in poor performance with new data.
2. An algorithm used in optimization to adjust the weights of a neural network in order to minimize error.
3. An architecture in neural networks responsible for combining input data in more complex ways to solve complicated problems.
5. A method used to prevent overfitting by simplifying the neural network architecture.
7. Method used to adjust neuron weights in neural networks to minimize error during training.
10. A type of learning where a neural network adjusts its internal parameters based on input-output pairs.
11. the process of finding the best combination of weights for a neural network architecture.
13. popular large-scale dataset used for training and testing image recognition algorithms.