Node embeddings

an graph G=(V,E)

where v is a node and e

ML

machine learning is used in

1. finicial services, --automate  mannuala tasks, identify and address systemic bias, detect fraud , identify investent opportunities hidden in available data
2. health care--patient risk identification, visual data processing for tumor detection, helped in fighting the Covid -19 pandamic,AI- disinfeting robots, fever detection in public places, virus tracking and deep learning for covid detection etc
3. oil and gas
4. Governent 
5. retail
6. trasportation

data center anagement

to save energy and improve service quality

 how to synchronise VM state?

stop and copy=== stop the source, copy the source Vm and 

qurantine to cloud computing

shibin@xjtu.edu.cn

whats cloud cmputing and trying to use it

covid -19

how to protect o self-clean yo hands, keep social distance and quarantine and ware masks

social distance and quarante most effect

but class cant be gone..unless u use online classes means lots of data. covid had increased dat via online thus big data. the servers dont have enough capacit to hold so much burst.

cloud computing comes in to help bse its elastic features and on demand in usage

cloud computing has grown faster than big data and machine learning

google is the champion in cloud computng

classic application of big data technology

bigdata computing  model and construction perspctive

BI-business intellengency

AI-artificial intelegency

Neuroscience background

deep learning-recurrent structure

experiment

theory

calculation

data

whole sample instead of sampling

efficuency rather than precision

related rather than causal

deep learning -any learning methor that can train a system with more than 2 or 3 non liear hidden layers

convolution neural network

sparse connectivity each neural only connects to part of the output of the previous layer

parameter sharing the neurons withdiffirent receptive fields can use the same set of parameters

less parameters than fully connected layer

deep learning

.deep learning

• a kind of solutions of machine learning
• types incude Arthur Samuel chess
• amazon personalization algorith
• self -driving examples
• biopsy feature (12Vs 9)

machine learning

why machine learning-recognizing patterns, recognizing anomalies, prediction

• massive parallelism, huge data volumes stoae, data distribution, highspeed networks,etc

  bigdata is every where.big data offers time advantage and data mining 

• tools for big data-bigdata techniques.

mapreduce, hadoop, locality sensitive hashing

data parallel and model parallel

• deep learning

• characterictics of big data

-volume- size and skill

velocity -geneerated at high speed

and variety, -variety like images, tables etc

veracity- uncertainty of the data

-it exceeds the processing capacity  

big data is data that exceeds the processing capacity of conventional database systems. the data is too big , moves to fast or doesnt fit the structures of your databas

• when big data is really a problem -- storage of big data, alot of things are becoming smart terminals- data from 50B devices, data centres wont fit into memory of single machine
• big data needs big models to extract understanding-(the processing arigorithims)

• tools for big data
• deep learning

challenges in big data:

1) problem with storage

More data allows us

to see new aspects, see better aspects, see different aspects

more data..more information. 

• whats big data- where does big data come from?

-examples facebook, an internet minute, smart phone users, big data is smilar to sall dat but bigger and requires diffirent approaches like techniques, tools and architctres to solve new problemms and old probles in abetter way.

it will bring challanges in volumes ,process and arigorithms 

-what is the value of big data

-helps to track adays movement/estimate adays routine 

-helps to know the emotion variations on national days

-helps with scene completion problems

• when big data is really a problem 
• tools for big data
• deep learning

data is mined so as to establish consumer emotion and trend.