Basic Understanding of Neural Networks

Question 1

What is the basic structure of a Neuron (Perceptron) in ANN?

Answer 1

A Neuron (Perceptron) consists of input values, weights, a bias, an activation function, and an output

The input values are multiplied by weights, summed, and then passed through the activation function.

Question 2

What is the purpose of an activation function in Neural Networks?

Answer 2

The activation function introduces non-linearity into the model, allowing it to learn complex patterns

Examples include ReLU, sigmoid, and tanh functions.

Question 3

What are the steps involved in training a Neural Network?

Answer 3

The steps include:
* Forward propagation
* Loss calculation
* Backpropagation
* Weight update

These steps are repeated for multiple epochs to optimize the model.

Question 4

How can we assess the performance of our model?

Answer 4

Performance can be assessed using metrics such as accuracy, precision, recall, F1 score, and loss

These metrics help evaluate how well the model predicts outcomes.

Question 5

Can you highlight the differences between Batch Gradient Descent and Stochastic Gradient Descent in the context of Machine Learning?

Answer 5

Differences include:
* Batch Gradient Descent uses the entire dataset for each update
* Stochastic Gradient Descent updates weights for each training example

Stochastic Gradient Descent is generally faster and can escape local minima more effectively.

Question 6

Which method is commonly used to determine optimal values for parameters like weights and biases in a Neural Network?

Answer 6

Gradient Descent is commonly used to optimize weights and biases

Variants include Mini-batch Gradient Descent, Adam, and RMSprop.

Question 7

What is a loss function, and why is it important?

Answer 7

A loss function quantifies the difference between predicted and actual outcomes, guiding the optimization process

Common loss functions include Mean Squared Error and Cross-Entropy Loss.

Question 8

What role do hyperparameters play in a Neural Network?

Answer 8

Hyperparameters control the learning process and architecture, such as learning rate, number of layers, and batch size

They must be tuned to improve model performance.

Question 9

What are the parameters of a Neural Network?

Answer 9

Parameters include weights and biases within the network

These parameters are adjusted during training to minimize the loss.

Question 10

How should you select the suitable format of a neural network (MLP, RNN, CNN, GNN) for a project?

Answer 10

Selection should be based on the nature of the data and the problem domain

MLP is used for structured data, RNN for sequential data, CNN for image data, and GNN for graph data.

Question 11

How do you select the most suitable setting for the loss function in ANN?

Answer 11

The selection depends on the type of problem being solved (e.g., regression or classification)

Different tasks may require different loss functions for optimal performance.