DeepLearning_GPT3_questions

b) L2 regularization. L2 regularization adds a penalty term to the loss function that encourages the model to learn small weight values.

a) L1 regularization. L1 regularization adds a penalty term to the loss function that encourages the model to learn sparse weight matrices.

d) To prevent overfitting. Early stopping is a technique that involves stopping the training of a model before it has completed all the epochs in order to prevent overfitting.

b) Dropout. Dropout is a regularization technique in which randomly selected neurons are dropped during training, which helps prevent overfitting.

c) They can suffer from the vanishing gradient problem. Multilayer perceptrons can suffer from the vanishing gradient problem, where gradients become very small as they are backpropagated through many layers. This can make training difficult and slow. While multilayer perceptrons are often computationally efficient and can be relatively easy to interpret, they do require labeled training dat

a) It is used to compute gradients of a loss function with respect to the weights of a neural network. Backpropagation is a widely used algorithm for computing gradients of a loss function with respect to the weights of a neural network. However, it is not guaranteed to find the global minimum of the loss function, it can be used with recurrent neural networks as well as feedforward neural networks, and it requires the use of activation functions.

Removing hidden layers. Removing hidden layers is not typically used as a method for avoiding overfitting in multilayer perceptrons. Regularization, dropout, and early stopping are all commonly used techniques for this purpose.

Softmax. While softmax is often used as the output activation function for multiclass classification problems, it is not typically used as an activation function for hidden layers in multilayer perceptrons.

All of the above. Early stopping, data augmentation, and dropout are all common techniques used to prevent overfitting in deep learning.

d) All of the above. Using mini-batches during training can lead to faster convergence to a good solution, improved generalization to new data, and reduction of overfitting.

d) Naive Bayes. Stochastic Gradient Descent (SGD), Adam, and RMSProp are all commonly used optimizers in deep learning, but Naive Bayes is not an optimizer, it is a classification algorithm.

Answer: B) The Perceptron Loss minimizes the negative sum of the dot product between weights and inputs for all misclassified examples. This can be mathematically defined as L(w) = -Σ [y_i(w^T x_i)] where y_i is the true label, x_i is the input, and w is the weight vector.