Unit-3 Neural Network Worksheet Multiple choice questions: 1. 2. Which of the following models works on continuous data? a) Classification c) Regression b) Modularization d) Reinforcement The job of which of the following layer is to acquire data and feed it to the neural network? a) Input c) Hidden b) 3. 4. 6. 7. 8. 9. d) Last In which of the following model, the dataset fed to the machine is labelled? a) Unsupervised learning c) Rule Based b) Learning Based d) Reinforcement Learning Which of the following are the core processing units of the network? a) Cells c) Brain b) 5. Output Neurons d) Machines What kind of datasets are primarily used by neural networks for solving problem? a) Mentioned c) Visual b) Continuous d) Large The input can be complex and __________ for ANN to use it to generate the desired output. a) Large c) Discrete b) Non-linear d) Visual In classification, you teach the machine to perform with what kind of data? a) Labelled c) Large b) Complex d) Continuous Which layer is present in between input and output layer? a) Middle c) Next b) Process d) Hidden Each node of the hidden layer has its own ______________ algorithm which it executes on the data received by the input layer. a) Machine learning c) Machine fed b) Rule based d) AI 10. 11. 12. Which of the following is artificially created efficient computing system designed to simulate the human brain? a) AI c) BNN b) ANN d) CNN Which of the following AI model gets trained on the data fed to it and then is able to design a model that is adaptive to change in data? a) Rule-based c) Learning based b) Machine learning d) Deep Learning c) d) Route detection All of the above The ANN is used for _____________. a) b) 13. 14. 15. Forecasting Character & voice recognition Which of these is a disadvantage of ANN? a) It can perform multiple jobs simultaneously b) It can be implemented in any application c) It has potential for high risk tolerance d) It has to be trained first to operate Classification & clustering works on ____________. a) Discrete data c) Structured data b) Continuous data d) Data set in ____________ type of AI model the algorithms generate a mapping function from the given data. a) Clustering c) AI models b) Classification d) Regression Fill-in the blanks: 1. _______________ are artificially created systems or networks. (ANN) 2. Neural network is a network of artificial neurons or _____________. (nodes) 3. ______________ enables AI systems to perform typical tasks such as predictions, classifications etc. (Deep Learning) 4. The structure of ANN is similar to ______________. (Human brain) 5. The key advantage of neural network is that they are able to extract ______________.(data features) 6. Neural network is a branch of ML which is known as ________. (Deep Learning) 7. The dataset used for classification are _______________. (Labelled) 8. ______________model works on discrete dataset. (clustering) 9. Neural Network is a mesh of multiple _____________________. (Hidden Layers) 10. The analogy of an Artificial Neural Network can be made with _____________? (Parallel Processing) Short/Long answer questions: Q1. What are the features of an Artificial Neural Network? Any Artificial Neural Network, irrespective of the style and logic of implementation, has a few basic features as given below. The Artificial Neural Network systems are modelled on the human brain and nervous system. • • • • They are able to automatically extract features without feeding the input by programmer. Every node of layer in a Neural Network is compulsorily a machine learning algorithm. It is very useful to implement when solving problems for very huge datasets. It can work with incomplete knowledge and may produce output even with incomplete information. Q2. Explain the relation between data size and model performance of an Artificial Neural Network. The basis for any kind of AI development is BIG DATASET. The performance of any AI based application depends on the data supplied. ANN models are also known as Learning models and are used for prediction purposes. These are mostly developed without paying much cognizance to the size of datasets that can produce models of high accuracy and better generalization. Although, the general belief is that, large dataset is needed to construct a predictive learning model. To describe a data set as large in size, perhaps, is circumstance dependent, thus, what constitutes a dataset to be considered as being big or small is somehow vague. In fact, the quantity of data partitioned for the purpose of training must be of good representation of the entire sets and sufficient enough to span through the input space. It must be authentic and relevant to give better model performance. Q3. What is an Artificial Neural Network? Explain the layers in an artificial neural network. Artificial Neural Network: Modeled in accordance with the human brain, a Neural Network was built to mimic the functionality of a human brain. The human brain is a neural network made up of multiple neurons, similarly, an Artificial Neural Network (ANN) is made up of multiple perceptrons. A neural network consists of three important layers: Input Layer: As the name suggests, this layer accepts all the inputs provided by the programmer. Hidden Layer: Between the input and the output layer is a set of layers known as Hidden layers. In this layer, computations are performed which result in the output. There can be any number of hidden layers Output Layer: The inputs go through a series of transformations via the hidden layer which finally results in the output that is delivered via this layer. Q4. What is the idea behind the creation of Artificial Neural Network? Artificial neural networks (ANNs) were designed to simulate the biological nervous system, where information is sent via input signals to a processor, resulting in output signals. ANNs are composed of multiple processing units that work together to learn, recognize patterns, and predict data Q5. Why do scientists struggle to replace the working of human brains into Artificial Neural Networks? The Artificial Intelligence misinformation epidemic centered around brains working like neural nets seems to be coming to a head with researchers pivoting to new forms of discovery – focusing on neural coding that could unlock the possibility of brain-computer interface. A neural network architecture is arranged into layers, wherein each layer consists of many simple processing units — nodes — further connected to several nodes in the layers above and below. Q6. Write about some of the uses of the artificial intelligence in everyday life. AI assists in every area of our lives, whether we’re trying to read our emails, get driving directions, or start a new business. Artificial intelligence is used in day-to-day activities such as: • Email communications • Social media • Web searching • Stores and services • Offline experiences Q7. How is the human brain different from the Artificial Neuron Network models? An artificial neural network has 10-1000 neurons in them, whereas a human brain has around 86 billion neurons in it. Both networks have different types of working and structure. ANN's have a predefined model, only the weights of connections can change during training. The neurons can neither be added nor removed Q8. How can Artificial Neural Networks improve decision making? Give examples. Artificial neural networks are well suited for prediction, forecasting, generalizing decisions based on previously processed data, classifying data, building meta models based on samples. In my knowledge, ANNs return outputs according to the entered inputs without explaining how such decision was made . With the advance of information technology, artificial neural networks (ANNs) are now a ready-to-use technology for all kinds of industries.
