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What is DNS? The essence of DNS is the invention of a hierarchical, domain-based naming scheme and a distributed database system for implementing the naming scheme. The primary use of DNS is for mapping host names and email destination to IP addresses but can also be used for other purposes. The DNS is a distributed database. It is used by TCP/IP application. We use the term distributed because no single site on the internet knows all the information. Each site maintains its own database of information and runs a server program that other system across the internet can query. The DNS provides the protocols that allow client and server to communicate with each other. Services Provided by the DNS:- There are several services provided by DNS-  1) Host Aliasing:-  A host with a complicated hostname can have one or more aliss names. 2) Mail Serve Aliasing:- DNS can be involved by a mail application to obtain the hostname for a supplied alias hostname as well as the IP address of the host. 3

1x1 Convolution  

Explain binary exponential back-off algorithm.                                   Or  Discuss any one algorithm for allocating a multiple access channel. To ensure that random back-off maintains stability, CSMA/CD uses a technique known as binary exponential back-off. Binary exponential back-off algorithm varies the limit on number of slot times. It works in the following manner- 1) if a stations frame collides for the first time, wait 0 or 1 time slot before trying again. 2) In case of colliding a second time, wait 0, 2 or 3 slot times. 3) If it collides third time, wait anywhere from 0 to 7 slots. 4) After 16 collisions, give up and report an error. Thus, as congestion increase, stations back-off by larger and larger amounts to reduce the probability of collision. This method clearly attempts to reduce excessive waits by keeping the number of possible time slots small. After all if two stations collide, there is a 50% opportunity they will succeed on the next attempt. If several stati

Difference between OSI Model and TCP/IP Model.   Or Difference between Open System Interconnection Model and Transmission Control Protocol/Internet Protocol Model.    1 2  

Overfitting in machine learning  Overfitting refers to a model that models the training data too well. Overfitting happen when a model learn the detail and noise in the training data to the extent that it negatively impacts the performance of the model an new data. This means that the noise or random fluctuations in the training data is picked up and learned as concept by the model. The problem is that these concepts do not apply to new data and negatively impact the model ability to generalize. For example, decision tree are a nonparametric machine learning algorithm that is very flexible and is subject to overfitting training data. This problem can be addressed by pruning a tree after it has learned in order to remove some of the detail it has picked up. Technique to reduce overfitting:- There are several techniques to reduce overfitting in machine learning. 1) Cross Validation 2) L1/L2 regulariszation 3) Feature Selection 4) Hold Out 5) Dropout 1) Cross Validation:- One of the most