Artificial Neural Networks

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Questions and Answers

What is the main purpose of the backpropagation algorithm in a neural network?

To modify the weights and biases of the neural network (correct)

To calculate the output of the hidden layers

To initialize the weights and biases of the neural network

To determine the mean squared error loss

What is the order of calculations in a feedforward neural network?

o1, h1, h2

h1, h2, o1 (correct)

o1, h2, h1

h2, o1, h1

What is the purpose of step 2 in the training algorithm?

To determine the mean squared error loss (correct)

To modify the weights and biases of the neural network

To initialize the weights and biases of the neural network

To calculate the output of the hidden layers

What are the weights and biases initialized in the training algorithm?

w1, w2, w3, w4, w5, w6, wb1, b2, b3 Signup and view all the answers

What is calculated in backpropagation after feedforward?

b3 Signup and view all the answers

What is the purpose of the repeat loop in the training algorithm?

To repeat the process for N times Signup and view all the answers

What is the first step in the process of a neuron?

Multiplying each input by a weight Signup and view all the answers

What is the purpose of an activation function?

To turn an unbounded input into a predictable output Signup and view all the answers

What is the range of the sigmoid function?

(0, 1) Signup and view all the answers

What happens to big negative numbers when passed through the sigmoid function?

They become 0 Signup and view all the answers

What is the output of the unit step function at x = 7?

1 Signup and view all the answers

What type of problem can a simple neuron solve?

Linear classifier problem Signup and view all the answers

What is the formula for combining the weighted inputs and bias?

w1* x1 + w2* x2 + b Signup and view all the answers

What is the value of the hyperbolic tangent function at x = 7?

0.9999 Signup and view all the answers

What is the primary limitation of a simple neuron (Perceptron)?

It can only classify linearly separable problems Signup and view all the answers

What is the solution to the XOR problem in neural networks?

Adding a hidden layer to the network Signup and view all the answers

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

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

What is the term for a neural network with multiple hidden layers?

Deep Learning Signup and view all the answers

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

To produce the final output of the network Signup and view all the answers

What is the first step in training a neural network?

Constructing the neural network topology Signup and view all the answers

How is the sex of an individual represented in the training data?

Male is represented as 0 and Female as 1 Signup and view all the answers

What is the purpose of the loss function in training a neural network?

To quantify how well the network is doing Signup and view all the answers

What is the output of the neural network in the given example?

y_pred Signup and view all the answers

What is the purpose of shifting the data in the training process?

To make the data easier to use Signup and view all the answers

Study Notes

Neural Networks

A neural network is a bunch of neurons connected together.
It can have multiple layers, including an input layer, one or more hidden layers, and an output layer.
A hidden layer is any layer between the input layer and output layer.

Training a Neural Network

The process involves three steps: initialize weights, repeat forward and backward passes, and modify weights.
Forward pass: calculate outputs from inputs using current weights.
Backward pass: calculate error and modify weights to minimize error.

Feedforward Neural Network

Calculate outputs from inputs using current weights.
Output of one layer is used as input to the next layer.

Back Propagation

Calculate error and modify weights to minimize error.
Calculate errors for each layer, starting from the output layer and moving backwards.

XOR Problem

A simple neuron (perceptron) cannot classify XOR problem.
Solution is to add a hidden layer to create a multilayer perceptron (MLP).

Multilayer Perceptron (MLP)

A feedforward neural network with one or more hidden layers.
Can solve non-linear problems like XOR.

Example Neural Network

Given a neural network with 2 inputs, 2 hidden neurons, and 1 output neuron.
Calculate output using given weights and activation functions.

Building Blocks: Neurons

A neuron takes inputs, applies weights, adds bias, and passes through an activation function.
Activation function is used to turn an unbounded input into a predictable output.

Activation Function

A commonly used activation function is the sigmoid function.
Sigmoid function outputs numbers in the range (0,1) and compresses (-∞, +∞) to (0,1).

Linear Classifier

A simple neuron can solve linear classifier problems like OR.
But it cannot solve non-linear problems like XOR.

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Description

This quiz covers the basics of artificial neural networks, including multilayer networks, feedforward networks, and back propagation. Test your knowledge of neural network concepts and algorithms.