AML Deep Learning & Neural Networks

Question 1

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

Accepted Answer

To add non-linearity to the network

Answer

The primary function of an activation function in a neural network is to introduce non-linearity into the model. Without activation functions, a neural network would only be able to learn linear transformations, regardless of its depth, limiting its ability to model complex relationships. By adding non-linearity, activation functions enable the network to learn and approximate highly complex, non-linear functions present in real-world data.

Question 2

Which of the following is a common activation function?

Accepted Answer

ReLU

Answer

ReLU (Rectified Linear Unit) is one of the most common and widely used activation functions in deep learning. It outputs the input directly if it is positive, otherwise, it outputs zero, making it computationally efficient. Sigmoid is also an activation function, while Mean Squared Error is a loss function and Dropout is a regularization technique.

Question 3

What does the term 'overfitting' refer to in deep learning?

Accepted Answer

The model performs well on training data but poorly on unseen data

Answer

Overfitting occurs when a deep learning model learns the training data too well, including its noise and specific details, rather than the underlying general patterns. This results in excellent performance on the training dataset but a significant drop in performance when the model is exposed to new, unseen data. The model fails to generalize because it has essentially memorized the training examples instead of learning robust features.

Question 4

What technique is used to reduce overfitting in neural networks?

Accepted Answer

Dropout

Answer

Dropout is a powerful regularization technique specifically designed to reduce overfitting in neural networks. During training, it randomly sets a fraction of neurons to zero at each update, effectively 'dropping out' these neurons. This prevents the network from relying too heavily on specific neurons and forces it to learn more robust and generalized features, improving its performance on unseen data.

Question 5

Which neural network is best suited for sequential data?

Accepted Answer

Recurrent Neural Network

Answer

Recurrent Neural Networks (RNNs) are specifically designed to process sequential data, where the order and context of elements are crucial. Unlike feedforward networks, RNNs have internal memory that allows them to retain information from previous steps in a sequence, making them ideal for tasks like natural language processing, speech recognition, and time series analysis. Convolutional Neural Networks (CNNs) are typically used for spatial data like images.

(AML) Advanced Machine Learning Certification Practice Test

(AML) Advanced Machine Learning Certification Practice Test

AML Deep Learning & Neural Networks