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Neural Networks Interview Questions
Let there are two neurons X and Y which is transmitting signal to another neuron Z . Then , X and Y are input neurons for transmitting signals and Z is output neuron for receiving signal . The input neurons are connected to the output neuron , over a interconnection links ( A and B ) as shown in figure .
simple neuron works
For above neuron architecture, the net input has to be calculated in the way.
I = xA + yB
where x and y are the activations of the input neurons X and Y. The output z of the output neuron Z can be obtained by applying activations over the net input.
O = f(I)
Output = Function ( net input calculated ) : The function to be applied over the net input is called activation function . There are various activation function possible for this.
1. Every new technology need assistance from the previous one i.e. data from previous ones and these data are analyzed so that every pros and cons should be studied correctly. All of these things are possible only through the help of neural network.
 
2. Neural network is suitable for the research on Animal behavior, predator/prey relationships and population cycles .
 
3. It would be easier to do proper valuation of property, buildings, automobiles, machinery etc. with the help of neural network.
 
4. Neural Network can be used in betting on horse races, sporting events, and most importantly in stock market.
 
5. It can be used to predict the correct judgment for any crime by using a large data of crime details as input and the resulting sentences as output.
 
6. By analyzing data and determining which of the data has any fault ( files diverging from peers ) called as Data mining, cleaning and validation can be achieved through neural network.
 
7. Neural Network can be used to predict targets with the help of echo patterns we get from sonar, radar, seismic and magnetic instruments.
 
8. It can be used efficiently in Employee hiring so that any company can hire the right employee depending upon the skills the employee has and what should be its productivity in future.
 
9. It has a large application in Medical Research .
 
10. It can be used to for Fraud Detection regarding credit cards , insurance or taxes by analyzing the past records .
Hybrid systems: A Hybrid system is an intelligent system that is framed by combining at least two intelligent technologies like Fuzzy Logic, Neural networks, Genetic algorithms, reinforcement learning, etc.
 
Types of Hybrid Systems : 
 
* Neuro-Fuzzy Hybrid systems
* Neuro Genetic Hybrid systems
* Fuzzy Genetic Hybrid systems
 
Neuro-Fuzzy Hybrid systems : The Neuro-fuzzy system is based on fuzzy system which is trained on the basis of the working of neural network theory. The learning process operates only on the local information and causes only local changes in the underlying fuzzy system. A neuro-fuzzy system can be seen as a 3-layer feedforward neural network. The first layer represents input variables, the middle (hidden) layer represents fuzzy rules and the third layer represents output variables. Fuzzy sets are encoded as connection weights within the layers of the network, which provides functionality in processing and training the model. 
Neuro-Fuzzy Hybrid systems
Neuro Genetic Hybrid systems : A Neuro Genetic hybrid system is a system that combines Neural networks: which are capable to learn various tasks from examples, classify objects and establish relations between them, and a Genetic algorithm: which serves important search and optimization techniques. Genetic algorithms can be used to improve the performance of Neural Networks and they can be used to decide the connection weights of the inputs. These algorithms can also be used for topology selection and training networks. 
Neuro Genetic Hybrid systems
Fuzzy Genetic Hybrid systems : A Fuzzy Genetic Hybrid System is developed to use fuzzy logic-based techniques for improving and modeling Genetic algorithms and vice-versa. Genetic algorithm has proved to be a robust and efficient tool to perform tasks like generation of the fuzzy rule base, generation of membership function, etc. 

Three approaches that can be used to develop such a system are: 
 
* Michigan Approach
* Pittsburgh Approach
* IRL Approach
Fuzzy Genetic Hybrid systems


Source : Geeksforgeeks
S.NO Soft Computing Hard Computing
1. Soft Computing is liberal of inexactness, uncertainty, partial truth and approximation. Hard computing needs a exactly state analytic model.
2. Soft Computing relies on formal logic and probabilistic reasoning. Hard computing relies on binary logic and crisp system.
3. Soft computing has the features of approximation and dispositionality. Hard computing has the features of exactitude(precision) and categoricity.
4. Soft computing is stochastic in nature. Hard computing is deterministic in nature.
5. Soft computing works on ambiguous and noisy data. Hard computing works on exact data.
6. Soft computing can perform parallel computations. Hard computing performs sequential computations.
7. Soft computing produces approximate results. Hard computing produces precise results.
8. Soft computing will emerge its own programs. Hard computing requires programs to be written.
9. Soft computing incorporates randomness . Hard computing is settled.
10. Soft computing will use multivalued logic. Hard computing uses two-valued logic.
S.NO. A.I. SOFT COMPUTING
1 Artificial Intelligence is the art and science of developing intelligent machines. Soft Computing aims to exploit tolerance for uncertainty, imprecision, and partial truth.
2 AI plays a fundamental role in finding missing pieces between the interesting real world problems. Soft Computing comprises techniques which are inspired by human reasoning and have the potential in handling imprecision, uncertainty and partial truth.
3 Branches of AI :

 

1. Reasoning
2. Perception
3. Natural language processing  
Branches of soft computing :

 

1. Fuzzy systems
2. Evolutionary computation
3. Artificial neural computing  
4 AI has countless applications in healthcare and widely used in analyzing complicated medical data. They are used in science and engineering disciplines such as data mining, electronics, automotive, etc.
5 Goal is to stimulate human-level intelligence in machines. It aims at accommodation with the pervasive imprecision of the real world.
6 They require programs to be written. They not require all programs to be written, they can evolve its own programs.
7 They require exact input sample. They can deal with ambiguous and noisy data.
Here is the step by step process on how to train a neural network with TensorFlow ANN using the API’s estimator DNNClassifier.
 
We will use the MNIST dataset to train your first neural network. Training a neural network with TensorFlow is not very complicated. 
 
You will proceed as follow :
 
Step 1: Import the data
Step 2: Transform the data
Step 3: Construct the tensor
Step 4: Build the model
Step 5: Train and evaluate the model
Step 6: Improve the model