Neural Networks Principles

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Questions and Answers

What is the primary function of a neural network after training?

To predict the output for a given new input (correct)

To process inputs in both forward and backward directions

To extract relevant features from the input data

To classify inputs into predefined categories

What type of neural network is typically used for image classification tasks?

Convolutional Neural Network (CNN) (correct)

Fully-Connected Neural Network (FCN)

Recurrent Neural Network (RNN)

Artificial Neural Network

What is the primary advantage of Fully-Connected Neural Networks (FCN)?

Large training data needed

Finding relations within sequential data

High accuracy in image recognition problems

Simple design (correct)

What type of data is typically used with Recurrent Neural Networks (RNN)?

Sequence data Signup and view all the answers

What is a common application of Neural Networks in Electrical Engineering?

Fault detection/classification in electric devices Signup and view all the answers

What is a disadvantage of Fully-Connected Neural Networks (FCN)?

Computationally expensive Signup and view all the answers

What is the primary building block of Convolutional Neural Networks (CNN)?

Filters Signup and view all the answers

What is a characteristic of Recurrent Neural Networks (RNN)?

The inputs are processed in both forward and backward directions Signup and view all the answers

What is the simplest form of the linear equation used in the example?

y = A * x1 + B * x2 Signup and view all the answers

What is the number of layers in the Neural Network represented in the figure?

3 Signup and view all the answers

What is the main difference between Shallow and Deep Neural Networks?

Number of hidden layers Signup and view all the answers

What is the maximum number of examples in the training data for Shallow Neural Networks?

1000 Signup and view all the answers

What is the purpose of the hidden layer in a Neural Network?

To find the mathematical relations between the training data sets Signup and view all the answers

What is the term for the process of finding the values of A, B, and C in the linear equation?

Training the Network Signup and view all the answers

What is the main function of a fully-connected neural network?

To model the relationship between input and output variables Signup and view all the answers

How many neurons are in the input layer of the Neural Network?

2 Signup and view all the answers

What is the equation that represents the improvement of the linear equation?

y = A * x1 + B * x2 + C Signup and view all the answers

What is the purpose of dividing the data into training and testing data?

To evaluate the performance of the neural network on unseen data Signup and view all the answers

What is the function of the hidden layer in a neural network?

To transform the input data into a more useful representation Signup and view all the answers

What is the name of the neural network architecture where every neuron in one layer is connected to every neuron in the subsequent layer?

Multi-Layer Perceptron (MLP) Signup and view all the answers

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

To introduce non-linearity into the neural network Signup and view all the answers

What is the term for the process of adjusting the neural network's parameters to minimize the error between the predicted and actual outputs?

Training Signup and view all the answers

What is the purpose of the output layer in a neural network?

To compute the output of the neural network Signup and view all the answers

What is the relationship between the inputs (x1, x2) and the output (y) in the illustrative example?

y = x1 + x2 Signup and view all the answers

Study Notes

Neural Network Principle

A neural network can predict an unknown output for a given new input after training.

Neural Network Categories

There are three main categories of neural networks:
- Fully-Connected Neural Networks (FCN)
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)

Fully-Connected Neural Networks (FCN)

Building blocks are neurons
Inputs are processed in the forward direction only
Deals with vector data inputs
Advantages: simple design
Disadvantages: computationally expensive

Convolutional Neural Networks (CNN)

Building blocks are filters
Filters extract relevant features from input using convolution operation
Deals with matrices/image data inputs
Advantages: high accuracy in image recognition problems
Disadvantages: large training data needed

Recurrent Neural Networks (RNN)

Building blocks are neurons with recurrent loop in the hidden layer
Inputs are processed in both forward and backward directions
Deals with sequence data inputs
Advantages: finding relations within sequential data
Disadvantages: having difficulties with long sequences

Electrical Applications of Neural Networks

Neural networks have been used in electrical engineering applications, including:
- Fault detection/classification in electric devices
- Fault detection/classification in power systems
- Load estimation for electric devices and power systems
- Optimization of solar and wind power generation
- Self-driving/autonomous cars

Fully-Connected Neural Networks (FCN) - How it Works

FCN is also known as dense neural networks or multi-layer perceptron (MLP)
Every neuron in one layer is connected to every neuron in the subsequent layer
Information flows from the input layer through one or more hidden layers to the output layer

Illustrative Example of FCN

A simple example of using FCN to find the relation between inputs (x1, x2) and output (y)
The computer assumes a linear/straight line equation to approximate the relation
The task of the network is to find the constants (A, B, C) in the equation