Deep Learning

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

• • To compute the gradient of the loss function

• • To introduce non-linearity into the model

• • To regularize the weights

• • To initialize the network parameters

Which optimization algorithm is commonly used for training deep neural networks?

• • Gradient Descent

• Random Search

• • C) K-Means

• • Newton's Method

What is the vanishing gradient problem in deep learning?

• • The loss function decreases too slowly during training

• • The gradients become too small, hindering the learning of lower layers

• • The model converges too quickly, leading to overfitting

• • The weights become too large, causing numerical instability

What is transfer learning in the context of deep learning?

• • Training a model on a source task and then applying it to a different target task

• Transferring weights from one layer to another within the same network

• • Learning from scratch without using pre-trained models

• • Sharing the learning experience between multiple neural networks

What is the purpose of the softmax activation function in the output layer of a classification neural network?

• • To introduce non-linearity into the model

• • To normalize the output values into probabilities

• • To reduce the dimensionality of the output

• • To prevent overfitting in the final layer

What is the main goal of deep learning?

• Unsupervised learning

• Feature extraction

• Training neural networks to learn from data and make predictions

• Reinforcement learning

Which deep learning architecture is well-suited for processing sequential data like natural language?

• CNN

• RNN

• LSTM

• GAN

Which loss function is commonly used for multi-class classification problems in deep learning?

• Mean Squared Error (MSE)
  

• Mean Absolute Error (MAE)
  

• Cross-Entropy Loss
  

• Hinge Loss

What is the primary limitation of using deep learning in cases with limited labeled data?

• The inability to use transfer learning
  

• The need for larger networks

• The risk of overfitting

• The requirement for more computational power

What is the purpose of the learning rate in the context of deep learning?

• To adjust the number of layers in the neural network
  

• To control the speed at which the model learns during training
  

• To determine the activation function to be used in the network

• To handle the imbalance in the training dataset