Neural Networks

What is the basic unit of a neural network?

• gene

• neuron

• atom

• cell

What is the purpose of an activation function in a neural network?

• To make the network slower

• To increase the linearity of the network

• Introduce non-linearity into the network

• To reduce the accuracy of the network

Explain the concept of backpropagation in neural networks.

• Backpropagation is a method used to train neural networks by adjusting the weights of the connections based on the error in the output.

• Backpropagation is a method used to increase the error in the output of neural networks.

• Backpropagation is a method used to randomly adjust the weights of the connections in neural networks.

• Backpropagation is a method used to ignore the error in the output of neural networks.

What is the role of weights in a neural network?

• Control the temperature of the neural network

• Adjust the strength of connections between neurons

• Determine the color of the neural network

• Provide energy to the neurons

What is the difference between supervised and unsupervised learning in the context of neural networks?

• Supervised learning uses labeled data for training, while unsupervised learning uses unlabeled data.

• Supervised learning requires human intervention, while unsupervised learning is fully automated.

• Supervised learning uses images, while unsupervised learning uses text data.

• Supervised learning is used for classification, while unsupervised learning is used for regression.

What is the vanishing gradient problem in neural networks?

• Gradients remaining constant throughout the layers

• Gradients becoming extremely small in earlier layers

• Gradients becoming extremely large in earlier layers

• Gradients disappearing in later layers

How does a convolutional neural network (CNN) differ from a regular neural network?

• A CNN uses convolutional layers to process spatial hierarchies in data, while a regular neural network uses fully connected layers to process data sequentially.

• A CNN has fewer layers than a regular neural network

• A CNN uses recurrent layers to process data, while a regular neural network uses convolutional layers to process data

• A CNN only works with numerical data, while a regular neural network can work with any type of data

What is the purpose of a bias in a neural network?

• To remove certain nodes from the network

• To shift the activation function by adding a constant value

• To multiply the input by a random value

• To divide the output by a constant value

Explain the concept of overfitting in the context of neural networks.

• Overfitting is when a neural network learns the training data too well, negatively impacting its ability to generalize to new, unseen data.

• Overfitting is when a neural network learns the training data too loosely, positively impacting its ability to generalize to new, unseen data.

• Overfitting is when a neural network learns the training data perfectly, improving its ability to generalize to new, unseen data.

• Overfitting is when a neural network learns the training data too slowly, impacting its ability to generalize to new, unseen data.

What are some common applications of neural networks in real-world scenarios?

• Building construction and interior design

• Cooking recipes and gardening tips

• Image and speech recognition, natural language processing, recommendation systems, and autonomous vehicles

• Weather forecasting and stock market predictions