Learning rule or Learning process is a method or a mathematical logic. It improves the Artificial Neural Network's performance and applies this rule over the network. Thus learning rules updates the weights and bias levels of a network when a network simulates in a specific data environment.

Applying learning rule is an iterative process. It helps a…

ContinueAdded by Sheetal Sharma on April 10, 2018 at 7:00pm — No Comments

**Applications of SVM in Real World **

SVMs depends on supervised learning algorithms. The aim of using SVM is to correctly classify unseen data. SVMs have a number of applications in several fields.

Some common applications of SVM are-

- Face detection – SVMc classify parts of the image as a face and non-face and create a square boundary around the face.

- Text and hypertext…

Added by Sheetal Sharma on August 17, 2017 at 11:30pm — No Comments

Learning of neural network takes place on the basis of a sample of the population under study. During the course of learning, compare the value delivered by output unit with actual value. After that adjust the weights of all units so to improve the prediction.

There are many Neural Network Algorithms are available for training …

ContinueAdded by Sheetal Sharma on August 15, 2017 at 7:00pm — No Comments

There are various Artificial Neural Network Models. Main ones are

- Multilayer Perceptron – It is a feedforward artificial neural network model. It maps sets of input data onto a set of appropriate outputs.
- Radial Basis Function Network – A radial basis function network is an artificial neural network. It uses radial basis functions…

Added by Sheetal Sharma on August 10, 2017 at 7:30pm — No Comments

Artificial Neural networks (ANN) or neural networks are computational algorithms.

It intended to simulate the behavior of biological systems composed of “neurons”. ANNs are computational models inspired by an animal’s central nervous systems. It is capable of machine learning as well as pattern recognition. These…

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Some of the machine learning applications are:

One of the most common uses of machine learning is image…

ContinueAdded by Sheetal Sharma on August 7, 2017 at 8:00pm — 1 Comment

**Hadoop – Introduction & features**

Let us start with what is Hadoop and what are Hadoop features that make it so popular.

Hadoop is an open-source software framework for distributed storage and distributed processing of extremely large data sets. Important features of Hadoop are:

Hadoop is an open source project. It means its code can be modified to business requirements.

In Hadoop, data is highly available and…

Added by Sheetal Sharma on July 31, 2017 at 7:30pm — No Comments

There is a huge hype of Big Data and its features, most of them have been summed up in 9 different Vs of Big data like Volume, Velocity, Variety, Veracity, Validity, Volatility, Value, Variability, Viscosity.

In a recently published white paper by credit reference agency Experian, a proposal has been given to add another “V” to the…

ContinueAdded by Sheetal Sharma on July 20, 2017 at 8:00pm — No Comments

**1.Objective**

First of all we will see what is R Clustering, then we will see the Applications of Clustering, Clustering by Similarity Aggregation, use of R amap Package, Implementation of Hierarchical Clustering in R and examples of R clustering in various fields.

**2. Introduction to Clustering in…**

Added by Sheetal Sharma on July 19, 2017 at 9:00pm — No Comments

- Learning Rules in Neural Network
- Real-Life Applications of Support Vector Machines
- Neural Network Algorithms - Learn How To Train ANN
- Artificial Neural Network Model
- Artificial Neural Network (ANN) in Machine Learning
- Top 9 Machine Learning Applications in Real World
- Limitations of Hadoop – How to overcome Hadoop drawbacks

- Top 9 Machine Learning Applications in Real World
- Artificial Neural Network (ANN) in Machine Learning
- Neural Network Algorithms - Learn How To Train ANN
- R Clustering – A Tutorial for Cluster Analysis with R
- Limitations of Hadoop – How to overcome Hadoop drawbacks
- Real-Life Applications of Support Vector Machines
- Learning Rules in Neural Network

- HDFS (1)
- Hadoop (1)
- Map (1)
- R (1)
- Reduce (1)
- clustering (1)
- data-flair.training (1)
- learning (1)
- machine (1)

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