shape attribute over arrays in Python

 shape attribute over arrays in Python

Python - use of slicing operator on string

 Python - use of slicing operator on string

Help Section in R language

 Help Section in R language

Assumptions in Machine Learning and Statistics ( A Comparison )

 Assumptions in Machine Learning and Statistics ( A Comparison )

what are some technology challenges for big data

 what are some technology challenges for big data

 

what are some technology challenges for big data

 

•  Storing Huge Volumes of Big Data

The very first and foremost challenge over the technology challenges of big data relates to the storing of huge quantities of data . As of now , there is no such computing machine with a storage thatis as big and enough to store the ongoing growing volume of data which has been a big cause of issue for most of the organisations engaged in the business of big data . Therefore , one should first try to store the huge quantity of data in manageable & inexpensive machines and then with upscaling prices and costs one could strive for better infrastructure . However , as it is customary with machines , machines are prone to getting failure at any random point of time and with more and more machines over the big data ecosystem , the chances of failure at any time may also becomes high . Each of the participative and implementable machines in the list could fail at some point or another and failure of a machine could entail a

loss of valuable data stored over them .

 

• So the first function of big data technology thus should be to store huge volumes of data within them and that too without incurring a high cost to the organization , while also combatting the risk of data loss . So all big data systems distribute data across a large cluster of inexpensive machines connected with each other over the big data network . This process also ensures that all of the data within the system is made failproof by ensuring that every piece of data is stored on multiple machines which would guarantee that at least one copy of the data is available to all the connected storage machines . All the above-mentioned processes are made fail-proof with the help of Hadoop which is a very well-known clustering technology of big data . Hadoop's data storage pattern is called as Hadoop Distributed File System (HDFS). This technology HDFS is built on the pattern's of Google's Big File Systems , which is designed to store billions of pages and sort the pages

to answer user search queries .

Training in Machine Learning

  Training in Machine Learning

 

 

Training in Machine Learning

========================

 

•  Training provides a learner algorithm with examples

of the desired inputs and also the results expected

from those inputs .The learner algorithm uses these

inputs to create a function

 

• Training in machine learning context is the process

where a learner algorithm maps a function to the data

and matches up with the inputs to their expected

Outputs

 

 

Back-propagation in machine learning

 Back-propagation in machine learning

 

Back-propagation in machine learning

===============================

 

•  Back-propagation or backward propagation of errors

is a method to determine the conditions under which

errors are removed form a neural network which is

built to resemble the human's neurons functions

by changing the weights and biases of the network

continually with a goal to arrive at an actual output

which would match the target output .

 

• The neurons present in the network catch the transmitted

information and relay the information along to

the next neuron in the line . And in this process , the

entire network is built for relaying the information from

the source of information propagation to the desired

target and in this way each neuron in the network is

shared a portion of the total information relayed and

as such all neurons keep passing information to next

neuron in line until the set of neurons create a final

Output .Thr total sum of errors at the rsult / target ise calculated by the method of Back-propagation

 

 

Technology Challenges for Big Data

Technology Challenges for Big Data

 

 

Question - Technology Challenges for Big Data

--------------------------------------------------------------

 

Ingesting Streams at an extremely fast pace . This

relates to the velocity of streaming of big data over

the enterprise systems . This velocity of big data

generally relates to the torrential and fast streams of

data . In some cases the velocity of the data streams

may be very large and fast to be stored , but still then

the pace of the data inflow should be monitored

and this can be done by creation of special purpose

ingesting systems that could open multiple number of

channels for receiving , utilisation and consumption

of the data . These ingesting systems could be used

for holding data in queues from which business

applications can read and process data at their own

pace and convenience.

 

The second layer and second most important purpose

to solve for big data systems is thus managing the

challenge posed by velocity of big data . And to deal

with this issue , special stream-processing engines

have been put in place where all the incoming data is

fed into a central queueing system over the network

of big data machines . From this system , a fork

shaped system sends data to the batch storage as well

as to the stream processing directions . These stream

processing engines can do the work of collection

of high velocity big data and send it to the batch

processing systems who stream the incoming data in

multiple batches and redistribute the volume among

the batch segregation systems . A most popular

system for this type of work handling is apache spark

which handles the work of streaming applications .

 

 

 

 

How does Machine Learning enable Artificial Intelligence to perform tasks

  How does Machine Learning enable Artificial Intelligence to perform tasks

 

 

How does Machine Learning enable Artificial

              Intelligence to perform tasks

=========================================

 

1) Machine Learning helps in detection of patterns in

all sorts of data sources

 

2) Machine Learning helps in creation of new models

based on the recognised patterns and behaviours

 

3) Machine Learning helps in making decisions

based on the success and failure of the patterns and

Behaviours

 

 

 

 

Data Handling technique of Machine Learning and Statistics

 Data Handling technique of Machine Learning and Statistics

 

 

Data Handling technique of Machine Learning and Statistics

=================================================

 

* Machine Learning works with Big Data in the form of networks and graphs ; raw data from sensors and internet , and the data collected is split into training and test data

 

* In Statistics , statistical models are created to create the prediction on smaller samples and further analysis for future

data based on earlier past data

Machine Learning requirements for Hardware

  Machine Learning requirements for Hardware

 

 

Machine Learning requirements for Hardware

=======================================

 

Machine Learning datasets require a huge amount

of memory . And , when such amount of memory is

required for vast amounts of data , then powerful

processors with multiple cores with high speeds would

also be necessitated . For this , better investments

in hardware for faster and efficient processing of the

datasets would also be required . So , for this sometimes

waiting for the model's solutions for best results

might take time for which scientists go for a useful

result in lieu of a right result when time is a constraint

The current state-of-the-art systems rely on Graphical

Processing Units(GPUs) to perform machine learning

tasks as they speed up the machine learning process

Considerably

 

 

 

A short summary on the concept of unsupervised learning

 A short summary on the concept of unsupervised learning 

A short summary on the concept of SVM algorithm in machine learning part -02

 A short summary on the concept of SVM algorithm in machine learning part -02

A short summary on the concept of SVM algorithm in machine learning

 A short summary on the concept of SVM algorithm in machine learning

A short summary on Supervised Learning in Machine Learning

 A short summary on Supervised Learning in Machine Learning

A short summary on Stochastic Gradient Descent learning in machine learning

 A short summary on Stochastic Gradient Descent learning in machine learning

A short summary on Softmax non-linearity activation function in machine learning

 A short summary on Softmax non-linearity activation function in machine learning

A short summary on the concept of Sensitivity in Statistics

 A short summary on the concept of Sensitivity in Statistics

A short summary on ROC curve and what does it stand for

 A short summary on ROC curve and what does it stand for 

Decision Making over Recurrent Neural Networks

 Decision Making over Recurrent Neural Networks