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Machine learning techniques are becoming increasingly more widespread in applications. We use apps based on machine learning every day, and one typical example is Apple’s Siri and for more info about machine learning examples visit our web page. – PowerPoint PPT presentation

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Title: About Machine Learning Examples


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About Machine Learning Examples
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Siri To begin with, Siri uses various
technologies, including question analysis,
natural language processing, machine learning,
and data mashups. Speech recognition, in
particular, involves spoken word conversion to a
sequence of words, also known as speech to text
(STT) or automatic speech recognition (ASR).
Speech recognition apps are available in many
forms and can be found in home appliances, cell
phones, data entry, medical transcriptions, air
traffic controllers, and more.
The training process of a speech recognition
systems demands several techniques, and the level
of complexity varies depending on the nature of
the training. Siri doesnt understand the English
language, but because it is based on a set of
specific templates that trigger specific actions,
it adapts making you believe its human. It
doesnt learn new things, but it can be expanded
and extended. It can only perform programmed
tasks.
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Cortana Microsofts Cortana is smart enough
to crack a joke and tell whether you should leave
early for work because theres a lot of traffic.
You cannot trust it completely, but its a start.
Cortana redefines the term virtual assistant
because it was built following a model called
dynamic learning. Cortana is an advanced
business intelligence (BI) analytics suite a
data storage package that includes information
management system, business intelligence
software, machine learning, all wrapped up in a
monthly service subscription.
Available on Microsofts Azure Cloud, Cortana
enables startups and businesses to completely
transform their data into intelligent actions. It
allows users to process data from a many
different data sources change that data, apply
machine learning and data mining to advanced
analytics techniques, and then extract actionable
information that enables businesses to take
timely, intelligent actions.
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Face Recognition Facebook Helps You Remember
the Name of Your Friends Another very cool
machine learning example is Facebooks newest
face recognition software. We know, its pretty
scary! How is it possible for Facebook to know
the name of your friends when you upload a group
picture if you havent even had the time to tag
them? Back in the day, Facebook recommended
users to tag their friends. All they had to do
was click on their face on the picture and type
their name to tag them. Facebook didnt know who
those people were. Now, when a photo is uploaded
onto a users profile, Facebook automatically
recommends that user tag their friends. And it
knows exactly who is who. Abracadabra? Or just
creepy?
The new technology is better known as face
recognition, and it uses machine learning
(Facebooks algorithms) to recognise familiar
faces in your friends list. Its pretty amazing,
and it can do it because it is a form of adaptive
technology. You probably tagged those friends
before in the past, right? Facebooks machine
learning mojo memorised those patterns, and
voila! Theres your answer.
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How Much Machine Learning is There in Search
Engines? Why do you think Google is so powerful?
Its no secret that machine learning is gaining
popularity across a wide variety of industries.
Not all programs are capable, though. It all
depends on how much data these programs have to
train their models. Thats the main reason
companies like Facebook and Google need as much
data as they can get. For instance, Google made
TensorFlow open-source. It is an excellent
software toolkit that helps build large-scale
machine learning apps. Giving that away, for
free, was a big deal. If you dont know what
TensorFlow is, imagine that its the same thing
that powers the almighty Google Translate.
However, without Googles substantial
mountains of data (in every possible language)
theres no way someone can compete with Google
Translate. What makes Google so powerful? Its
DATA. Google has a lot of data it can leverage,
and all that data can be adapted and taught to
perform specific tasks.
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Everyone knows Google Translate, and many people
depend on it. But how can the website translate
between over 100 human languages so seamlessly?
Sure, there are sentences, grammar and structure
problems to most translations but nevertheless,
its still accurate. At the core of Google
Translate theres a technology called machine
translation, which has managed to change
completely the way people communicate.
In the last two years, the approach to basic
machine translation has been completely rewritten
by deep learning. Theres a pretty fascinating
piece of technology hidden behind this
breakthrough, known as sequence to sequence
learning. The technique is pretty powerful and
it can be used to fix many problems. Weve seen
what it can do for Google Translate. But did you
know that the same algorithms can be applied to
describe pictures and write AI chat bots?
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PayPal Uses Machine Learning to Detect
Fraud Detecting Fraud within banks is another
Machine Learning Example, lets explorer in depth
how PayPal uses Machine Learning. Fraud detection
matters a lot because it addresses a problem
thats urgent, and machine learning can help
combat fraud. PayPal for example an
international online payments system that serves
as an alternative to conventional payment modes
and deals with money transfers online has dealt
with its fair share of fraudulent transactions
over the years. So its only natural to think
that the company would do just about anything to
combat fraud.
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For More Detail Contact s Or Visit Our Website..!
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Phone Number 44 207 127 4304 Email
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Url www.itenterprise.co.uk
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