Feed-Forward Neural Networks

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Questions and Answers

What is the primary objective of adjusting weights in a feed-forward neural network?

To increase the number of input units

To reduce the number of hidden units

To change the function represented by the network (correct)

To implement sigmoid activation functions

What is the alternate name of the Multilayer Perceptron model?

Backpropagation algorithm (correct)

Deep Learning Architecture

Feed-forward Neural Network

Single Layer Perceptron

What is the derivative of the sigmoid function g(x) = 1/(1 + e^(-x))?

g(x) / (1 - g(x))

g(x) ∗(g(x) - 1)

g(x) / (g(x) - 1)

g(x) ∗(1 - g(x)) (correct)

What is a characteristic of hidden units in a Multilayer Perceptron?

They are typically chosen by hand Signup and view all the answers

What is a requirement for deep learning algorithms to solve complicated issues?

Large amounts of computing power and information Signup and view all the answers

What happens when the bias weight W0,i is changed in a neural network?

The threshold location is moved Signup and view all the answers

What is a common implementation of the activation function in a Multilayer Perceptron?

Sigmoid, ReLU, or TanH functions Signup and view all the answers

What is the simplest type of artificial neural network?

Single Layer Perceptron Signup and view all the answers

What can a Multi Layer Perceptron (MLP) learn that a Single Layer Perceptron cannot?

Both linear and non-linear functions Signup and view all the answers

What is represented by a range of architectures in deep learning?

Solutions for a range of problem areas Signup and view all the answers

What is the primary characteristic of a Feed-forward Neural Network?

The information moves in only one direction, from input nodes to output nodes Signup and view all the answers

Who showed that every Boolean function can be implemented?

McCulloch and Pitts Signup and view all the answers

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

To decide whether a neuron should be activated or not Signup and view all the answers

What is the function of the bias weight in a single layer perceptron?

To shift the activation function Signup and view all the answers

What is the output of the step function or threshold function when x is greater than or equal to 0?

+1 Signup and view all the answers

What is a simplified model of real neurons, used to develop understanding of what networks of simple units can do?

Output is a “squashed” linear function of the inputs Signup and view all the answers

What determines whether a neuron is activated or not in a neural network?

The input value compared to the threshold value Signup and view all the answers

What is the name of the function that decides whether a neuron should be activated or not?

Activation Function Signup and view all the answers

What is the process of prediction in a neural network using simpler mathematical operations?

Implementing Logical Functions Signup and view all the answers

What is the type of neural network where the output of one layer is used as input to the next layer?

Feed-forward Neural Network Signup and view all the answers

Study Notes

Feed-forward Neural Network (FNN)

A parameterized family of nonlinear functions
Information moves in only one direction: from input nodes to output nodes, with no cycles or loops
Types: Single Layer Perceptron (no hidden layer, can only learn linear functions) and Multi Layer Perceptron (one or more hidden layers, can learn non-linear functions)

Activation Functions

Decide whether a neuron should be activated or not based on input value and threshold value
Types:
- Step function or threshold function g(x)=1 if x>=0, 0 otherwise
- Sigmoid function g(x)=1/(1 + e^(-x)) and g'(x)=g(x) ∗(1− g(x))
- ReLU, TanH, etc.

Weight Adjustment

Changing weights changes the function of the neural network
Adjusting bias weight W0,i moves the threshold location

Deep Learning Architectures

A spectrum of architectures for a range of problem areas
Require large amounts of computing power and information to solve complicated issues

Implementing Logical Functions

McCulloch and Pitts: every Boolean function can be implemented using neural networks
Examples: AND, OR, NOT gates implementation using neural networks

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Description

Quiz on feed-forward neural networks, covering their structure and function, including parameterized nonlinear functions. Topics include neural network architecture and matrix operations.