Artificial Neurons and Weighted Inputs

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Why are different weights assigned to the inputs in an artificial neuron?

To emphasize the relative importance of the different inputs (correct)

To ensure all inputs are equally important in calculating the output

To strengthen the less important inputs

To weaken the more important inputs

What is the mathematical form of the operation performed by an artificial neuron?

$x_1w_1 + x_2w_2 + x_3w_3$

$x_1w_1 + x_2w_2 + x_3w_3 + b$

$f(x_1w_1 + x_2w_2 + x_3w_3 + b)$ (correct)

All of the above

What is the key difference between an artificial neuron and linear regression?

Artificial neurons use weights and bias, while linear regression uses only coefficients

Artificial neurons can model non-linear relationships, while linear regression can only model linear relationships

Artificial neurons apply an activation function, while linear regression does not

Both b and c (correct)

How are artificial neurons arranged in an Artificial Neural Network (ANN)?

In multiple layers, with connections only between adjacent layers Signup and view all the answers

What is the role of the bias term in an artificial neuron?

It is used to shift the activation function to the left or right Signup and view all the answers

What is the purpose of the activation function in an artificial neuron?

All of the above Signup and view all the answers

What determines the number of neurons in the output layer in a neural network?

The number and type of problem to be solved Signup and view all the answers

In binary classification, how many neurons are typically present in the output layer?

One Signup and view all the answers

What is the purpose of introducing non-linearity in neural networks?

To introduce a nonlinear transformation to learn complex patterns Signup and view all the answers

Which function scales the input value between 0 and 1 in neural networks?

Sigmoid function Signup and view all the answers

What is the range of values output by the Tanh function in neural networks?

-1 to 1 Signup and view all the answers

In a regression problem, how many neurons are typically found in the output layer of a neural network?

One Signup and view all the answers

What is the function of a synapse in a neuron?

It is where information is transmitted between neurons. Signup and view all the answers

How are inputs to a neuron weighted before being summed in the cell body?

They are strengthened or weakened based on their importance. Signup and view all the answers

What is the role of the soma in a neuron?

Processing summed inputs and sending them through axons. Signup and view all the answers

In artificial neurons, what happens to the inputs received before they are processed?

They are weighted based on their importance. Signup and view all the answers

Which of the following best describes the activation function of a neuron?

It introduces non-linearity into the network. Signup and view all the answers

What distinguishes neurons in artificial neural networks from linear regression models?

Neurons in artificial neural networks can handle non-linear relationships, unlike linear regression models. Signup and view all the answers

Artificial neurons are arranged by layers.

True Signup and view all the answers

Nuerons in the same layer do not have any connections.

True Signup and view all the answers

which one is not a typical ANN layers?

Forward Layer Signup and view all the answers

The number of neurons in the input layer is the number of inputs we feed to the network

True Signup and view all the answers

There is no computaion in the input layer. it is just used for passing information from the outside world to the network.

True Signup and view all the answers

Any layer between the inpout layer and the output layer is called ___________.

Hidden Layer Signup and view all the answers

The input layer identifies the pattern in the dataset.

False Signup and view all the answers

__________ identifies the pattern in dataset and is responsible for deriving complex relationships between input and output.

Hidden Layer Signup and view all the answers

The network is called a ___________________ when we have many hidden layers.

Deep Nueral Network (DNN) Signup and view all the answers

The number of neurons in the output layer is based on the number and the types of the problems to be solved.

True Signup and view all the answers

_______________ is used to introduce non-linearity in neural networks.

Activation Function Signup and view all the answers

The aim of the activation function is to introduce a nonlinear transformation to learn the complex underlying patterns in data.

True Signup and view all the answers

The _____________ scales the input values between 0 and 1.

Sigmoid Function Signup and view all the answers

Sigmoid Function = 1 / (1+ exp(-x))

True Signup and view all the answers

The ____________ outputs the values between -1 and +1

Hyperbolic tangent (tanh) Function Signup and view all the answers

____________ outputs the values from 0 yo infinity.

ReLU (Rectified Linear Unit) Function Signup and view all the answers

the sang for being zero for all negatives values is a problem called ________ and a neuron is said to be dead if it always outputs zero.

dying ReLU Signup and view all the answers

_____________ is a variant of the ReLU function that solves the dying ReLU problem. The value of alpha typically se to 0.01

Leaky ,ReLU Function Signup and view all the answers

ELU (Exponential Linear Unit) is like Leaky ReLU has a small slope for negative values.

True Signup and view all the answers

__________ is basically the generalization of the sigmoid functions.

The Softmax Function Signup and view all the answers

The Softmax Functions is usually applied to the final layer of the network and while performing multi-class classification tasks. It gives the probabilities of each class for being output and thus , the sum of softmax values will always equal 1.

True Signup and view all the answers

softmax function = exp(x) / sum(exp(x)) [0,j]

True Signup and view all the answers

Artificial Neurons and Weighted Inputs

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Questions and Answers

Why are different weights assigned to the inputs in an artificial neuron?

What is the mathematical form of the operation performed by an artificial neuron?

What is the key difference between an artificial neuron and linear regression?

How are artificial neurons arranged in an Artificial Neural Network (ANN)?

What is the role of the bias term in an artificial neuron?

What is the purpose of the activation function in an artificial neuron?

What determines the number of neurons in the output layer in a neural network?

In binary classification, how many neurons are typically present in the output layer?

What is the purpose of introducing non-linearity in neural networks?

Which function scales the input value between 0 and 1 in neural networks?

What is the range of values output by the Tanh function in neural networks?

In a regression problem, how many neurons are typically found in the output layer of a neural network?

What is the function of a synapse in a neuron?

How are inputs to a neuron weighted before being summed in the cell body?

What is the role of the soma in a neuron?

In artificial neurons, what happens to the inputs received before they are processed?

Which of the following best describes the activation function of a neuron?

What distinguishes neurons in artificial neural networks from linear regression models?

Artificial neurons are arranged by layers.

Nuerons in the same layer do not have any connections.

which one is not a typical ANN layers?

The number of neurons in the input layer is the number of inputs we feed to the network

There is no computaion in the input layer. it is just used for passing information from the outside world to the network.

Any layer between the inpout layer and the output layer is called ___________.

The input layer identifies the pattern in the dataset.

__________ identifies the pattern in dataset and is responsible for deriving complex relationships between input and output.

The network is called a ___________________ when we have many hidden layers.

The number of neurons in the output layer is based on the number and the types of the problems to be solved.

_______________ is used to introduce non-linearity in neural networks.

The aim of the activation function is to introduce a nonlinear transformation to learn the complex underlying patterns in data.

The _____________ scales the input values between 0 and 1.

Sigmoid Function = 1 / (1+ exp(-x))

The ____________ outputs the values between -1 and +1

____________ outputs the values from 0 yo infinity.

the sang for being zero for all negatives values is a problem called ________ and a neuron is said to be dead if it always outputs zero.

_____________ is a variant of the ReLU function that solves the dying ReLU problem. The value of alpha typically se to 0.01

ELU (Exponential Linear Unit) is like Leaky ReLU has a small slope for negative values.

__________ is basically the generalization of the sigmoid functions.

The Softmax Functions is usually applied to the final layer of the network and while performing multi-class classification tasks. It gives the probabilities of each class for being output and thus , the sum of softmax values will always equal 1.

softmax function = exp(x) / sum(exp(x)) [0,j]

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