In this section you will learn some important Neural Network Class 9 Questions and Answers. This Neural Network Class 9 Questions and Answers is divided in following parts. 

 

Neural Network Class 9 Questions and Answers (MCQs)

Neural Network Class 9 Questions and Answers (True and False)

Neural Network Class 9 Questions and Answers (Fill in the Blanks)

Neural Network Class 9 Questions and Answers (Short Questions)

Multiple Choice/ Objective Type Questions

 

1. It is the basic working unit of the brain.

a) Neuron

b) Electron

c) Prton

d) None of the above

 

Answer- a) Neuron

 

2. A “neuron” in an artificial neural network is a __________ function that collects and classifies information according to a specific architecture.

 

a) Statistical

b) Mathematical

c) Text

d) Graphical

 

Answer- b) Mathematical

 

3. A perceptron is a single-layer neural network. It consists of______ main parts.

a) two

b) three

c) four

d) five

 

Answer- a) two

 

4. In the artificial neural network, forward propagation delivers the:

 

a) Error value

b) Predicted value

c) Fixed value

d) Unpredicted value

 

Answer- b) Predicted value

 

5. In the artificial neural network, backward propagation delivers the:

a) Error value

b) Predicted value.

c) Unpredicted value.

d) Fixed value

 

Answer- a) Error value

Neural Network Class 9 Questions and Answers

6. In a human neuron, it is a long, cable-like projection of the cell carries the electrochemical message (nerve impulse) along the length of the cell.

a) Soma

b) Dendritis

c) Axon

d) None of the above

 

Answer- c) Axon

 

7. In a human neuron, these are small, branchlike projections of the cell make connections to other cells.

a) Soma

b) Dendritis

c) Axon

d) None of the above

 

Answer- b) Dendritis

 

8. In a human neuron, it sums all the incoming signals to generate input

a) Soma

b) Dendritis

c) Axon

d) None of the above

 

Answer- a) Soma

 

9. In this learning, you train the machine using data which is well labelled.”

a) Unsupervised

b) Supervised

c) Reinforcement

d) None of the above

 

Answer- b) Supervised

 

10. This type of learning takes place when a machine can analyse data patterns previously unspecified by humans.

a) Unsupervised

b) Supervise

c) Reinforcement

d) None of the above

 

Answer- a) Unsupervised

 

Neural Network Class 9 Questions and Answers

State True and False for the statements

 

1. The neuron is the basic working unit of the brain. [True]

 

2. A “neuron” in an artificial neural network is a mathematical function that collects and classifies information according to a specific architecture. [True]

 

3. A perceptron is a single-layer neural network. It consists of main parts, including input values, weights and bias, net sum, and an activation function. [True]

 

4. In the artificial neural network, two propagation functions work in a Neural Network: forward propagation that delivers the “error value” and backward propagation that delivers the “predicted” value.” [False]

 

5. In a human neuron, the axon is a long, cable-like projection of the cell carries the electrochemical message (nerve impulse) along the length of the cell. [True]

 

6. In a human neuron, dendrites sums all the incoming signals to generate input. [False]

 

7. In a human neuron, somas are small, branchlike projections of the cell make connections to other cells. [False]

 

8. In Supervised learning, you train the machine using data which is well “labelled.” [True]

 

9. Unsupervised learning takes place when a machine can analyse data patterns previously unspecified by humans. [True]

 

10. Reinforcement learning is an approach that helps a machine learn by rewarding desirable actions and penalising undesirable ones. [True]

Neural Network Class 9 Questions and Answers

Fill in the blanks

 

1. ______Networks are loosely modelled after how neurons in the human brain behave. [Neural]

 

2. A Neural Network is divided into multiple__________. [layers]

 

3. In a neural network each layer is further divided into several blocks called___________ [nodes]

 

4. The first layer of a Neural Network is known as the________layer. [input]

 

5. __________layers are the layers in which the whole processing occurs. [Hidden]

 

6. Single-layer Neural Network is called a___________. [Perceptron]

 

7.___________ function decides, whether a neuron should be activated or not by calculating weighted sum and further adding bias to it. [Activation]

 

8. Neural networks which consist of more than three layers of neurons (including the input and output layer) are called ________Networks. [Deep]

 

9. ___________propagation that delivers the “predicted value”. [Forward]

 

10. Deep learning uses __________ with multiple cores rather than central processing units (CPUs). [GPUs]

 

Neural Network Class 9 Questions and Answers

Answer the following Questions:

 

  1. Define Artificial neural network. How it is not comparable with human neural network.

Artificial Neural Network (ANN):

It’s a computational model inspired by human neural networks, consisting of interconnected nodes (neurons). Each neuron processes inputs and generates output signals; however, unlike their human counterparts, ANNs do not offer as much flexibility, adaptability, context awareness or intuition compared with human neural networks.

 

  1. Write down the function of Hidden layers in a neural network.

Hidden Layers in Neural Networks:

Hidden layers play an invaluable role in neural networks by aiding in learning complex patterns from input data. They capture nonlinear relationships between input and output that would otherwise remain hidden. In addition, hidden layers help process and transform information successively stages so as to make the network stronger and capable of solving more complicated problems.

 

Neural Network Class 9 Questions and Answers

  1. Write about Preceptron.

Perceptron:

This artificial neuron used in early neural network models, taking multiple inputs with assigned weights to generate output based on an activation function. While useful for simple binary classification tasks, its use becomes limited when confronted with complex problems requiring multiple layers.

 

  1. Write about components of human neutron.

 

Components of Human Neuron:

Each human neuron comprises of a cell body (soma), dendrites for receiving inputs, an axon for sending output, and synapses to connect it to other neurons in its network. Neurons transmit electrical and chemical signals across neural circuits in our bodies so we can communicate effectively.

 

  1. What is the difference between supervised and unsupervised learning?

 

Difference Between Supervised and Unsupervised Learning:

Supervised learning involves training a model using labeled data with correct answers provided, in which case the model learns to make predictions based on given inputs and known outputs. By contrast, unsupervised learning deals with unlabeled data requiring it to find its own patterns without explicit guidance to detect hidden structures or clusters within it.

 

Neural Network Class 9 Questions and Answers

 

  1. What is Reinforcement learning.

 

Reinforcement Learning:

Reinforcement learning is a form of machine learning in which an agent learns by interacting with its environment. Feedback in the form of rewards or penalties are then received depending on its actions; eventually the goal is to find strategies or policies which maximize cumulative rewards over time.

 

  1. Write use of Deep learning in the field of Entertainment.

 

Use of Deep Learning in Entertainment:

Deep learning has many applications in the entertainment industry. It can be used for personalized movie recommendations like those found on Netflix; advanced image and speech recognition to improve virtual reality gaming experiences; generating realistic graphics and special effects in movies or video games to make entertainment content more immersive; as well as deep learning algorithms helping create realistic special effects enhancing immersive viewing experience for movies or video games, which makes entertainment content even more engrossing and enjoyable.

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