Kips Class 9 AI Unit 3: Introduction to Neural Network

Unit 3: Introduction to Neural Network

Question and Answer
Question 1: Write briefly about Artificial Neural Network.
Answer:

1. Artificial Neural Network is a computational model based on the structure and function of biological neural network.
2. Artificial Neural Network is like an artificial human nervous system for receiving, processing and transmitting information in terms of computer science.
3. Artificial Neural Network is the imitation of Human nervous system.
4. Artificial Neural Network has incredible ability to learn from data and from environment. That makes the first choice of machine learning scientist.
5. Artificial Neural Network is the heart of the products such as Self driving cars, image recognition, etc.

Question 2: Write some applications of Artificial Neural Network.

Answer:

1. Image processing - Ex: Self driving cars
2. Character recognition - Ex: Scanning cheques in banks
3. Speech Recognition - Ex: Google Assistant
4. Natural language Processing - Ex: Siri
5. Forecasting - Ex: Google Maps
6. Medicine - Ex: Mammograms
7. Agriculture - Ex: Gap Production
8. Government -
9. Defence - Ex: Artificial Bird eye
10. Security - Face recognition

Question 3: Explain the structure of human neuron and artificial neural network.
Answer:

Components of Human Neuron:

Major components of human neuron are:
1. Dendrite: It takes input from the other neuron in the form of electrical impulse.
2. Cell body: It generates inferences received from those inputs and decides what action to take.
3. Axon terminal: It transmits the output in the form of electrical impulse.

Components of Artificial Neural Network:
Major Components of ANN are:
1. Input layer: It communicates with the external environment that presents a pattern to the neural network. Its job is to deal with all the inputs only. This input gets transferred to the hidden layers. Every input neuron should represent some variable that has an influence over the output of the neural network. The training observations are fed through these neurons.
2. Hidden layers: These are the intermediate layers between input and output layers which helps the neural network learn the complicated relationships involved in the data. Its job is to process the inputs obtained by its previous layer. So it is the layer which is responsible for extracting the required feature from the input data.
3. Output layer: It collects & transmits the information accordingly in way it has been designed to give. The pattern presented by the output layer can be directly traced back to the input layer. The final output is extracted from previous hidden layers.

Question 4: Explain Struggles in creating brain like neural network.
Answer:
Creating brain like neural networks is not easy due to the following reasons:
1. Problem of structure
This problem deals with finding out which neurons are connected to which other neurons which is difficult to determine. To make things more interesting, the connections between neurons in human beings are constantly changing.

2. Problem of weights and threshold
Each edge has a different weight associated with it. Further the neuron will only send a signal if it is stimulated beyond a set threshold. This threshold is also different for different neurons. For making things more interesting, the neurons in human brain tend to continually change the weights and thresholds.

3. Functioning of neurons might be complex
Neurons receiving signals from multiple neurons might get a combined stimulus that is more than its threshold. However, these signals need not be received at the same time. They have to be received within a short period. For dealing with all these issues, scientists and experts have proposed different kinds of neural networks.

Question 5: How do neural network differ from conventional computing?
Answer:

Conventional Computer

Artificial Neural Networks

1. A serial computer has a central processor that can address an array of memory locations where data and instructions are stored.

2. Computations are made by the processor reading instruction as well as any data that is required from memory addresses. The instruction is then executed, and the results are saved in a specific memory location, as required.

3. In a serial system (and a standard parallel one as well), the computational steps are deterministic, sequential, and logical, and state of a given variable can be tracked from one operation to another.

1. In comparison, Artificial Neural Networks are not sequential or necessarily deterministic.

2. There are no complex central processors, instead, there are many simple ones that generally do nothing more than taking weighted sum of their inputs from the other processors.

3. Artificial Neural Networks don’t execute programme instructions. They respond in parallel (either stimulated or actual) to the pattern of inputs presented to it.

4. There are also no separate memory addresses for storing data. Instead, the information is stored in the overall activation ‘state’ of the network.

5. ‘Knowledge’ is thus represented by the network itself, which is quite literally more than the sum of its components.


Question 6: Explain how neural networks learn.
Answer:
Neural Networks use three ways of learning:
1. Supervised learning
In this type of learning, the networks are trained to provide the correct output by using several example inputs. For example, the network can be trained on the data of students and marks. After training, the network will be able to calculate the marks that the student will get by analysing different elements like attendance, class participation, past marks, assignment quality etc.

2. Unsupervised learning
In this type of learning, the neural network is only provided with inputs. It doesn’t receive any information regarding the output. The system by itself evaluates and finds out how different elements are related to it. This is the method used for solving clustering problems, estimation problems, self-organising maps etc.

3. Reinforcement learning
In this type of learning, the neural networks learn based on the feedback received by it. Positive feedback helps the system to recognise right or correct output, and negative feedback helps the system to recognise incorrect or wrong output.

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