NCA AI & Deep Learning Frameworks

Question 1

What is the purpose of a neural network in deep learning?

Accepted Answer

To model complex data relationships

Answer

The primary purpose of a neural network in deep learning is to model and learn complex patterns and relationships within data. Inspired by the human brain, these networks consist of interconnected layers of artificial neurons that process information, allowing them to perform tasks like classification, regression, and pattern recognition, often with high accuracy on large and intricate datasets.

Question 2

Which activation function outputs values between 0 and 1?

Accepted Answer

Sigmoid

Answer

The Sigmoid activation function outputs values that are squashed into a range between 0 and 1. This characteristic makes it particularly useful in the output layer of binary classification problems, where the output can be interpreted as a probability. While once popular in hidden layers, its vanishing gradient problem has led to ReLU being preferred for those layers.

Question 3

What is overfitting in deep learning?

Accepted Answer

Model learns noise and poorly generalizes

Answer

Overfitting in deep learning occurs when a model learns the training data too well, including its noise and specific idiosyncrasies, rather than the underlying general patterns. Consequently, while the model performs exceptionally on the training set, its performance significantly degrades when presented with new, unseen data, indicating poor generalization ability. This often happens when a model is too complex for the amount of training data available.

Question 4

Which framework is developed by Google for machine learning?

Accepted Answer

TensorFlow

Answer

TensorFlow is an open-source machine learning framework developed by Google. It is widely used for building and training various types of neural networks and other machine learning models. TensorFlow provides a comprehensive ecosystem of tools, libraries, and community resources for research and production deployment across different platforms.

Question 5

What is backpropagation used for?

Accepted Answer

Weight updates using gradients

Answer

Backpropagation is a fundamental algorithm used in training artificial neural networks to efficiently calculate the gradients of the loss function with respect to the network's weights. These gradients indicate the direction and magnitude by which the weights should be adjusted to minimize the loss. By iteratively updating the weights based on these gradients, backpropagation enables the network to learn from its errors and improve its performance.

(NCA) NVIDIA-Certified Associate Practice Test

(NCA) NVIDIA-Certified Associate Practice Test

NCA AI & Deep Learning Frameworks