Brain and Neural Networks Quiz

15 Questions

What is the approximate weight of an adult human brain?

1.3 kg

How many neurons does an average human brain have?

86 billion

In Artificial Neural Networks, how many layers must every network have?

At least three layers

In which learning system do Artificial Neural Networks not receive information about expected outputs?

Unsupervised learning

What do Artificial Neural Networks modify for learning?

Weights associated with edges and threshold of nodes

What is the problem of structures in creating brain-like neural networks?

The difficulty in determining the constantly changing connections between neurons

How many output layers are required for constructing an Artificial Neural Network?

One

In which learning system do the Artificial Neural Networks not receive any information regarding the expected outputs?

Unsupervised learning

What happens in backpropagation based Artificial Neural Networks learning systems?

The output gets compared with the intended output

For learning, what do Artificial Neural Networks modify?

The weights associated with edges and threshold of nodes

What is the main function of a loss function in a neural network?

Defines the accuracy of the neural network with respect to a training data set and sample output

What does backpropagation do in an Artificial Neural Network?

Calculates the error and propagates it back to earlier layers

What determines how quickly a neural network updates its parameters?

Learning Rate

Which type of deep learning model is commonly applied in Computer Vision?

Convolutional Neural Networks (CNN)

In which layer of an Artificial Neural Network does no processing occur?

Input layer

Study Notes

Human Brain

The approximate weight of an adult human brain is around 1.4 kg (3 lbs).

Artificial Neural Networks

An average human brain has approximately 100 billion neurons.
There is no specific requirement for the number of layers in an Artificial Neural Network.
In Unsupervised Learning, Artificial Neural Networks do not receive information about expected outputs.
Artificial Neural Networks modify weights and biases for learning.
The problem of structures in creating brain-like neural networks is the von Neumann bottleneck.
A minimum of one output layer is required for constructing an Artificial Neural Network.
In Unsupervised Learning, Artificial Neural Networks do not receive any information regarding the expected outputs.
In backpropagation-based Artificial Neural Networks learning systems, the error is propagated backwards to adjust the model's parameters.
Artificial Neural Networks modify weights and biases for learning.
The main function of a loss function in a neural network is to measure the difference between the model's output and the actual output.
Backpropagation in an Artificial Neural Network is used to compute the gradients of the loss function with respect to the model's parameters.
The learning rate determines how quickly a neural network updates its parameters.
Convolutional Neural Networks (CNNs) are commonly applied in Computer Vision.
No processing occurs in the Input Layer of an Artificial Neural Network.

Test your knowledge of the human brain and artificial neural networks with this quiz. From brain weight to neural network structure, challenge yourself with fascinating facts about the brain and its artificial counterpart.

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