Introduction to Neural Networks and their use?

Introduction to Neural Networks and their use?

An Artificial Neural Network is an information processing model that is inspired by the way our brain process information. The basic idea behind a neural network is to copy in a simplified but reasonably faithful way lots of densely interconnected brain cells inside a computer so you can get it to learn things, make decisions, and recognize patterns in a human like way. One of the most wonderful thing neural networks is that it does not need to be programmed, it learns all by itself through examples, just like the brain.

Neural networks use a very different approach for solving problems from the one used by conventional computers. The conventional computers use the algorithmic approach, which means the computer follows the set of instructions in order to solve a problem. The computer needs to be fed with particular set of instructions to solve a problem, without the instructions it cannot solve the problem.Where on the other hand, neural networks process information in the same way as our brain does. The network is composed of highly interconnected processing elements called neurons, which work in parallel to solve a particular problem. In short we can say that neural networks learn from examples. They need not be programmed to perform a specific task. The examples must be carefully selected otherwise useful time is wasted and also the network might not work correctly.

A typical neural network has artificial neurons called units which vary in range from few dozen to hundreds, thousands, or even millions and are arranged in a series of layers, each of which connects to the layer on either side. These units have been divided into three categories, which are: Input Units, designed to receive various forms of information from outside world that the network will learn about, recognize, or otherwise process.

Other units sit on the opposite side of the network and signal how it responds to the information it learned, these are known as Output Units. In between the output and input units there lie one or more layers of Hidden Units, which together form the majority of the Artificial Brain. Most of the neural networks are fully connected, which means each hidden unit and each output unit is connected to every unit in the layers either side.

Neural networks, have a remarkable ability to derive meaning from complex or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. A trained neural network can be regarded as an expert in the category of information it has been given to analyze. This expert can then be used to provide projections given new situations of interest and answer “what if” questions. Neural Networks can also be used in the following fields:

Adaptive Learning: This includes the ability to learn on how to perform the task based on the data provided for training or initial experience. Like, Object detection and recognition application require large amount of data to train the model and perform the testing to validate about the object.

Self-Organization: An artificial neural network can create its own organization or representation of the information it receives during learning. Now days, multiple AI Companies are in the beginning stage to provide AI Services and Products.

Real Time Operation:Artificial Neural Networks computations may be carried out in parallel, and special hardware devices are being designed and manufactured which take advantage of this capability. There are various Real-time applications such as Stock Market Prediction System, Sports/Gaming Betting System,  E-Commerce Recommendation System.

Fault Tolerance via Redundant Information Coding: Partial destruction of a network leads to the corresponding degradation of performance. However, some network capabilities may be retained even with major network damage.

Conclusion: The computing world has a lot to gain from neural networks. Their ability to learn by example makes them very flexible and powerful. Furthermore there is no need to devise an algorithm in order to perform a specific task; i.e. there is no need to understand the internal mechanisms of that task. They are also very well suited for real time systems because of their fast response and computational times which are due to their parallel architecture. Neural networks also contribute to other areas of research such as neurology and psychology. They are regularly used to model parts of living organisms and to investigate the internal mechanisms of the brain. Therefore, neural networks do not perform miracles. But if used sensibly they can produce some amazing results.

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Top 10 Algorithms of Machine Learning

In this modern world technical revolution has taken place thus artificial intelligence and machine learning has come into existence because of its accurate predictions. However day by day it will gain more attention in industries. Thus first of all as an engineer’s we should be well known to these advanced techniques of machine learning and its algorithms.

Importance of Machine Learning: Machine learning is a field of Artificial Intelligence, which is allowed to software applications for making accurate results. Algorithms are built through which input is received and after statistical analysis output value is predicted. Because the algorithms are trained from dataset and thus learn from data finally improved results are predicted. Machine learning algorithms can be supervised, unsupervised and reinforcement learning. Let’s discuss them one by one: 

Supervised learning: In supervised learning algorithms training of model is done using some previous dataset. It requires humans for providing input and output. With some previous dataset training of model is done. After completion of training, algorithm applies the learnt things for prediction of new data.

Unsupervised learning: For more complex problems unsupervised learning is used. Interferences of dataset from input data is drawn. It is done for unlabelled data so cluster analysis method is used i.e from data it finds patterns from unclassified data. 

Reinforcement learning:  This type of learning is based on self-learning process. Machine learns its behavior by using feedback from the environment to maximize its performance.

10 algorithms of Machine Learning:  There are some machine learning algorithms for beginners available. These algorithms are:

  1. Linear Regression: For statistical technique linear regression is used in which value of dependent variable is predicted through independent variables? A relationship is formed by mapping the dependent and independent variable on a line and that line is called regression line which is represented by Y= a*X + b.

Where Y= Dependent variable (e.g weight).

X= Independent Variable (e.g height)

b= Intercept and a = slope.

  1. Logistic Regression: In logistic regression we have lot of data whose classification is done by building an equation. This method is used to find the discrete dependent variable from the set of independent variables. Its goal is to find the best fit set of parameters. In this classifier, each feature is multiplied by a weight and then all are added. Then the result is passed to sigmoid function which produces the binary output. Logistic regression generates the coefficients to predict a logit transformation of the probability. These machine learning algorithms are mostly used for Stock market Prediction systems.
  1. Decision Tree: It belongs to supervised learning algorithm. Decision tree can be used to classification and regression both having a tree like structure. In a decision tree building algorithm first the best attribute of dataset is placed at the root, and then training dataset is split into subsets. Splitting of data depends on the features of datasets. This process is done until the whole data is classified and we find leaf node at each branch. Information gain can be calculated to find which feature is giving us the highest information gain. Decision trees are built for making a training model which can be used to predict class or the value of target variable.
  1. Support vector machine: Support vector machine is a binary classifier. Raw data is drawn on the n-dimensional plane. In this a separating hyperplane is drawn to differentiate the datasets. The line drawn from centre of the line separating the two closest data points of different categories is taken as an optimal hyperplane. This optimised separating hyperplane maximizes the margin of training data. Through this hyperplane, new data can be categorised.
  1. Naive-Bayes: It is a technique for constructing classifiers which is based on Bayes theorem used even for highly sophisticated classification methods. It learns the probability of an object with certain features belonging to a particular group or class. In short, it is a probabilistic classifier. In this method occurrence of each feature is independent of occurrence another feature. It only needs small amount of training data for classification, and all terms can be pre-compute thus classifying becomes easy, quick and efficient.
  1. KNN: This method is used for both classification and regression. It is among the simplest method of machine learning algorithms. It stores the cases and for new data it checks the majority of the k neighbors with which it resembles the most. KNN makes predictions using the training dataset directly.
  1. K-means Clustering: It is an unsupervised learning algorithm used to overcome the limitation of clustering. To group the datasets into clusters initial partition is done using Euclidean distance. Assume if we have k clusters, for each cluster a centre is defined. These centres should be far from each other, and then each point is examined thus added to the belonging nearest cluster in terms of Euclidean distance to nearest mean, until no point remains pending. A mean vector is re-calculated for each new entry. The iterative relocation is done until proper clustering is done. Thus for minimizing the objective squared error function process is repeated by generating a loop.
Final results of the K-means clustering algorithm are:
  • The centroids of the K clusters, which are used to label new entered data.
  • Labels for the training data.
  1. Random Forest: It is a supervised classification algorithm. Multiple numbers of decision trees taken together forms a random forest algorithm i.e the collection of many classification tree. It can be used for classification as well as regression. Each decision tree includes some rule based system. For the given training dataset with targets and features, the decision tree algorithm will have set of rules. In random forest unlike decision trees there is no need to calculate information gain to find root node. It uses the rules of each randomly created decision tree to predict the outcome and stores the predicted outcome. Further it Calculate the votes for each predicted target. Thus high voted prediction is considered as the final prediction from the random forest algorithm.
  1. Dimensionality Reduction Algorithms: It is used to reduce the number of random variables by obtaining some principal variables. Feature extraction and feature selection are types of dimensionality reduction method. It can be done by PCA, Principal component analysis is a method of extracting important variables from large set of variables. It extracts the low dimensionality set of features from high dimensional data. It is used basically when we have more than 3 dimensional data.
  1. Gradient boosting and Ada Boost Algorithms: Gradient boosting algorithm is a regression and classification algorithm. AdaBoost only selects those features which improves predictive power of the model. It works by choosing a base algorithm like decision trees and iteratively improving it by accounting for the incorrectly classified examples in the training set. Both of algorithms are used for the boosting of the accuracy of predictive model.

In the nutshell, we can say that machine learning is one of the top-rating technologies these days. Machine learning as a service is in trending. In this article, we have discussed 10 basic algorithms of machine learning for the beginners. By using these algorithms we can develop machine learning applications within few hours and days.

Why Machine Learning Services are getting Maximum Attention?

Machine learning is one the trending topic these days. Right now, it is a catchword in the field of technology. It defines major representations that how computer can learn in future. Basically Machine learning algorithms are trained with the help of “training set” data.  By using this machine learning algorithm, it gives answer to the questions. For example, in training dataset you will have given pictures of dog to the computer. Some pictures will say, “This is a dog” or some will say, “This is not a dog”. Then you can show a number of new pictures and it would start searching which pictures were of dogs. Every picture which is identify as a correctly or incorrectly gets added to the training set. In this way program efficiently gets “smarter” and better by completing its task over time.

A couple of years back; it was the time of social, mobile, cloud and analytics etc. Although these technologies are still important as they have very good place in digital strategies.  Nowadays big hype is Artificial Intelligence, Internet of Things, Big Data, Machine learning services etc.  It is observed from the various surveys that Artificial Intelligence is the future of Growth.  There are some Artificial Intelligence Consulting companies available which provide number of services in this field. Machine learning is a part of Artificial Intelligence.

Let’s discuss Machine Learning Services which are attention seeker. These services are as follow:

  1. Fraud Detection: In numerous fields, machine learning is much better to spot fraud detection cases. Fraud management has been very aching for the commercial and banking sectors. Due to plethora of payment channel such as credit/debits cards, kiosks, smartphones etc., this menace is increasing day by day. In the similar way, criminals have found out loopholes in these channels. So, it is becoming difficult for the businessman to confirm transactions. However, Data Scientists have been successful in solving this problem with the help of machine learning. For instance, PayPal is using machine learning to fight with these raiders. This company has tools which compare all the transactions and easily distinguish between legitimate and non-legitimate transactions between sellers and customers.
  1. Recommendations: If you are regular user of Netflix and Amazon, then you must be familiar with use of this term. Intelligent algorithms of machine learning are monitoring  your all the activities and compare it with the other number of users and as a result show you which thing you would like to buy. These product recommendation systems are getting smarter every time. Suppose you want to buy light shade jeans of a particular brand, during your search it will also recommend you light shades jeans of some other brands. It will definitely make your shopping better with number of choices.
  1. Natural Language Processing (NLP): NLP is an emerging trend which is almost used in every field. Natural Language with Machine learning algorithms can stand for agents of customer service and in fast way route customers to the information they need. Mostly it is used to translate incomprehensible legalese in contracts into basic language and help prosecutors to handle large volumes of evidence to prepare for a case.
  1. Healthcare: Machine Learning algorithms can practice more information and find out more patterns than the humans can do. Machine learning can recognize risk factors in a better way for sickness in a large population area. A company build Disease Prediction System name “www.AIvaid.com” which based on machine learning algorithm that is capable to diagnose the human health conditions and predict the disease report. There are plenty of symptoms and diseases dataset has been added and working it wonderful. Along with that, Personalized medicine is also one of the effective treatments which relay on the health data of the individual combined with predictive analytics is a latest research and diligently correlated to better disease assessment. For this purpose, supervised learning is used which permits physicians to list out  from more limited sets of diagnoses based on the  symptoms and genetic information.
  1. Smart Cars: IBM recently surveyed top executives and it is concluded that we would see smart cars on road by 2025. It will learn about its owner as well as its environment and integrate it with Internet of Things.  Thus it will automatically adjust its internal setting such as audio, temperature, seat position automatically based on the driver and itself fix problems, drive itself and also give advice about traffic and conditions of the road.

In the nutshell, we can say that Machine Learning is a buzzword in the world of technology. Machine learning services are wooing more customers due to its smart learning techniques. Self-learning algorithms are now routinely embedded in mobile and online services. Researchers are getting massive gains in processing power and the data streaming from digital devices and connected sensors to improve AI performance. For many organizations, providing machine learning services can be challenging. When machines and human solve problems together and learn from each other, AI full prospective can be achieved.