Kips Class 9 AI Unit 3: Introduction to Neural Network

Unit 3: Introduction to Neural Network

Question 1: Define node, weak AI systems.
Answer:

• Node: Artificial Neural Networks use simplified models of neuron in the brain. These neurons are known as nodes.
• Weak AI systems: It is the intelligence that mimics intelligence but does not process it or in other words acts intelligently without having any intelligence.

Question 2: Write about ANN.
Answer:
A node can take several inputs and provide outputs. Each input will have the desired output. Each input will be okay as long as it provides the desired output. Providing undesired output will mean that the neuron has made a mistake. For correcting the mistake, the neuron will have to change its weights and thresholds.
In Artificial Neural Network, nodes or neurons are arranged in layers. There is a minimum of 3 layers; Input Layer, Hidden Layer, and Output Layer.
The data enters the ANN through an input layer. The input layer communicates data to one of the hidden layer. The hidden layers processes the data and sends it to another hidden layer (if there are more than one hidden layers) or the output layer. The output layer provides the output.

Question 3: Mention any four uses of ANN.
Answer:
Four uses of ANN are
• Voice recognition system
• Handwriting recognition
• Language translation
• Automated driving vehicles

Question 4: What is the idea behind the creation of ANN?
Answer:
Artificial Neural Networks (ANNs) are an attempt to copy the working of the interconnected cells in human brain inside the computer. The basic idea is to create a system, based on human brains, so that the computer can learn, recognise, and make human-like decisions. The power of the neural network lies in the fact that it doesn’t require any programming. Instead they learn i.e. by processing data.

Question 5: Explain the structure of ANN and HNN.
Answer:

• Human Neural Networks (HNN)
→ Each neuron of the brain is made up of a cell body. Cell bodies are very tiny.
→ There are several dendrites attached to the cell body and a single axon.
→ The dendrites carry information from outside the cell to the cell.
→  The axon carries the information away from the cell body.
• Artificial Neural Networks (ANN)
→ A node can take several inputs and provide outputs. Each input will have the desired output. Each input will be okay as long as it provides the desired output. Providing undesired output will mean that the neuron has made a mistake. For correcting the mistake, the neuron will have to change its weights and thresholds.
→ In Artificial Neural Network, nodes or neurons are arranged in layers. There is a minimum of 3 layers; Input Layer, Hidden Layer, and Output Layer.
→ The data enters the ANN through an input layer. The input layer communicates data to one of the hidden layer. The hidden layers processes the data and sends it to another hidden layer (if there are more than one hidden layers) or the output layer. The output layer provides the output.

Question 6: Explain the struggles in creating ANN.
Answer:
The struggles in creating ANN are
• The Problem of Structure
This problem deals with finding out which neurons are connected to which other neurons. This is difficult to determine. The connections between neurons in human beings are constantly changing.
• The Problem of Weights and Thresholds
Each edge has a different weight associated with it. Further the neuron will only send a signal if it is stimulated beyond a set of threshold. This threshold is also different for different neurons. The neurons in the human brains tend to continually change the weights and thresholds.
• The Functioning of Neurons might be complex
The neurons receiving signals from multiple neurons might get a combined stimulus that is more than its thresholds. They have to be received within a short period.

Question 7: How do Neural Networks differ from conventional computer?
Answer:

• Conventional Computer
→ A serial computer has a central processor that can address an array of memory locations where the data and instructions are stored.
→ Computations are made by the processors reading instructions as well as any data that is required from memory address. The instruction is then executed, and the results are then saved in a specific memory location, as required.
→ In a serial system, the computational steps are deterministic, sequential and logical and the state of a given variable can be tracked from one operation to the other.
• Neural Networks
→ In comparison, ANNs are not sequential or necessarily deterministic.
→ There are no complex central processors, instead, there are many simple ones that generally do nothing more than taking the weighted sum of their inputs from the other processor.
→ ANNs do not execute programme instructions. They respond in parallel to the patterns of inputs presented to it.
→ There are also no separate memory address for storing data. Instead, information is stored in the overall activation ‘state’ of the network.
→ ‘Knowledge’ is thus represented by the network itself which is quite literally more than the sum of its components.

Question 8: Explain how neural networks learn.
Answer:
Neural networks use three ways of learning
• Supervised learning: In this type of learning, networks are trained to provide the correct output by using several example inputs.
• Unsupervised learning: In this type of learning, the neural network is only provided with inputs. It does not receive any information regarding the output.
• Reinforcement learning: In this type of learning, the neural networks learn based on the feedback received by it.

Question 9: Write any four applications of neural networks.
Answer:
Four applications of neural networks are
• for capturing associations or discovering regularities with a set of pattern
• where the volume, number of variables or diversity of the data is very high
• if the relationships between the variables are vaguely understood
• if the relationship are difficult to describe adequately with conventional approaches.

Question 10: Does ANN systems needs to be programmed? Justify.
Answer:
No, the ANN systems doesn’t require any programming. The ANN consists of an input layer, hidden layer, and output layer. They usually contain multiple hidden layers. These layers are made up of several interconnected nodes and these nodes have an inbuilt ‘activation function’. The communication between the nodes depends of the weights and thresholds. The ANN use some sort of learning rules for modifying the weights and thresholds based on the inputs received by it. This learning can stop once the training has been completed, or can continue throughout the life of the project. This method of learning is similar to the method use by human beings to learn i.e. based on experience or data.

Question 11: Explain the importance of feedback in the learning process of ANN.
Answer:
The neural networks learn through an element of feedback. The feedback tells the network if it has made a mistake or performed correctly. One of the ways of learning involves backpropagation. In this system, the output of the neural networks is compared with the intended output. The weights and thresholds of the different nodes are changes to reduce the gap between the intended output and actual output. This process is repeated with every input, and the system continues to learn.

Question 12: Explain the flow of information through Neural Networks.
Answer:
The information that flows through the networks can take place under two distinct circumstances:
• When the system is learning or is under training
• When the system is in operation
The system will continue to learn as long as it can change the weights and thresholds of the nodes. The project developers can stop this learning when they think that the system has learned enough. The information received in the system is received by the input layer. The input layer, in turn, triggers the hidden unit. The hidden layer, in turn, triggers the output layer for delivering the solutions. This is one of the most common neural networks designs being implemented or in development.

Question 13: Explain backpropagation and feedforward network.
Answer:

• Backpropagation: In this system, the output of the neural networks is compared with the intended output. The weights and thresholds of the different nodes are changes to reduce the gap between the intended output and actual output. This process is repeated with every input, and the system continues to learn.
• Feedforward network: The information received in the system is received by the input layer. The input layer, in turn, triggers the hidden unit. The hidden layer, in turn, triggers the output layer for delivering the solutions. This is one of the most common neural networks designs being implemented or in development.

Question 14: How can ANN improve decision-making? Give an example.
Answer:
The ANN can improve its decision-making by learning from real-time data sent to them. For example, Radars used by the Indian defence forces. These radars use all the past data collected by the defence force throughout India and use this data for automatically identifying the enemy aircrafts. Such systems keep on learning from the real-time data sent to them from all the connected radars. It means that all the radars in the network are able to identify the new aircrafts as soon as a single one of them makes connections. These systems are also able to make predictions based on past and real-time data on probable enemy aircraft intrusions.

20 comments:

  1. Replies
    1. Thanks a lot Venkat for this beautiful website

      Delete
    2. Yeah it's beautiful ❤️

      Delete
    3. Thanks! Do share and subscribe our site for latest updates!

      Delete
  2. Anonymous31 July, 2021

    Please post 10th class also

    ReplyDelete
    Replies
    1. Thanks for reaching us out! You can find the solutions of Class 10 Artificial Intelligence by clicking on the link below:

      https://vs.eyeandcontacts.com/search/label/Class%2010%20Kips%20Artificial%20Intelligence

      Delete
    2. Hi,

      Thank you for visiting our site! We couldn't understand what are you talking about... Could you please explain briefly here.

      If you found our website helpful, you can share it with your friends! You can also follow us on:
      • Facebook: https://business.facebook.com/EducationWithVS
      • Twitter: https://twitter.com/EducationWithVS
      • Pinterest: https://in.pinterest.com/EducationWithVS

      Delete
  3. Replies
    1. Hi,

      Thank you for visiting our site! We couldn't understand what are you talking about... Could you please explain briefly here.

      If you found our website helpful, you can share it with your friends! You can also follow us on:
      • Facebook: https://business.facebook.com/EducationWithVS
      • Twitter: https://twitter.com/EducationWithVS
      • Pinterest: https://in.pinterest.com/EducationWithVS

      Delete
    2. yes please post for part a employability skills too

      Delete
    3. Ok sure! We'll post the employability skills part too!

      Delete
  4. Good questions! Please post some extra questions too.

    ReplyDelete
    Replies
    1. Thanks! Sure, do subscribe our site to receive our latest notifications!

      Delete
  5. OH MAH LAWRD I HAVE PREBOARDS THIS IS SAVING ME

    ReplyDelete
  6. class 9 ai Entrepreneurship

    ReplyDelete
  7. Thank you very much for the help 😀

    ReplyDelete
    Replies
    1. 😃 If you like vs.eyeandcontacts.com and enjoy visiting the solutions, please tell your friends on Facebook, Twitter, Instagram etc! Thank you.

      Delete

Buy from our Store

Visit our VS Store to get Extra Discounts! Only for our Beloved users!

Visit here

Also read!

• Class 6

Class 6 Computer Kips Solutions
Class 6 Gul Mohar English Solutions
→ Class 6 Map Pointing Solutions
Class 6 NCERT Civics Solutions
Class 6 NCERT Geography Solutions
Class 6 NCERT History Solutions
Class 6 NCERT Maths Solutions
Class 6 NCERT Science Solutions

• Class 7

Class 7 Computer Kips Solutions
Class 7 Gul Mohar English Solutions
English Grammar Class 7
English Writing Skills Class 7
Class 7 Map Pointing Solutions
Class 7 NCERT Civics Solutions
Class 7 NCERT Civics Notes
Class 7 NCERT Civics Extra Questions
Class 7 NCERT Geography Solutions
Class 7 NCERT Geography Notes
Class 7 NCERT Geography Extra Questions
Class 7 NCERT History Solutions
Class 7 NCERT History Notes
Class 7 NCERT History Extra Questions
Class 7 NCERT Maths Solutions
Class 7 NCERT Science Solutions
Class 7 NCERT Science Notes
Class 7 NCERT Science Extra Questions

• Class 8

Class 8 Kips Artificial Intelligence Solutions
Class 8 Gul Mohar English Solutions
English Grammar Class 8
English Writing Skills Class 8
Class 8 Map Pointing Solutions
Class 8 NCERT Civics Solutions
Class 8 NCERT Civics Notes
Class 8 NCERT Civics Extra Questions
Class 8 NCERT Geography Solutions
Class 8 NCERT Geography Notes
Class 8 NCERT Geography Extra Questions
Class 8 NCERT History Solutions
Class 8 NCERT History Notes
Class 8 NCERT History Extra Questions
Class 8 NCERT Maths Solutions
Class 8 NCERT Science Solutions
Class 8 NCERT Science Notes
Class 8 NCERT Science Extra Questions
Class 8 NCERT Science Exemplar
Class 8 NCERT Science MCQs

• Class 9

Class 9 Kips Artificial Intelligence Solutions
Class 9 Map Pointing Solutions
Class 9 NCERT Civics Solutions
Class 9 NCERT Geography Solutions
Class 9 NCERT History Solutions
Class 9 NCERT Maths Solutions
Class 9 NCERT Science Solutions

• Class 10

Class 10 Kips Artificial Intelligence Solutions
Class 10 NCERT English Solutions
→ Class 10 Map Pointing Solutions
Class 10 NCERT Civics Solutions
Class 10 NCERT Civics Notes
Class 10 NCERT Civics Extra Questions
Class 10 NCERT Geography Solutions
Class 10 NCERT Geography Notes
Class 10 NCERT Geography Extra Questions
Class 10 NCERT History Solutions
→ Class 10 NCERT History Notes
Class 10 NCERT History Extra Questions
Class 10 NCERT Maths Solutions
Class 10 NCERT Science Solutions
Class 10 NCERT Science Notes
Class 10 NCERT Science Extra Questions

• Computer languages
Python Basics

• Extra Activities
Extra Activities

• Extra Knowledge
Extra Information
General Knowledge
Historical Places in India
Latest technology
Physical Sciences
Facts

• Mathematics quick links
Mathematical Terms
Maths Tricks

HomeSearch Top