What is Deep Learning?

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WHAT IS DEEP LEARNING?
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Table Of Content
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What is Deep Learning?
Example of Deep Learning.
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Careers in deep learning.
How does it work?
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WHAT IS DEEP LEARNING?
Deep learning is a subset of machine learning
where artificial neural networks, algorithms
inspired by the human brain, learn from large
amounts of data. Similarly to how we learn from
experience, the deep learning algorithm would
perform a task repeatedly, each time tweaking it
a little to improve the outcome. We refer to
'deep learning' because the neural networks have
various (deep) layers that enable learning.
The amount of data we generate every day is
staggering--currently estimated at 2.6
quintillion bytes--and it's the resource that
makes deep learning possible. Since deep learning
algorithms require an abundance of data to be
able to learn from, this growth in data creation
is the primary reason why the capabilities of
deep learning have grown over the last few years.
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How does it work?
Deep Neural Networks have multiple layers of
interconnected artificial neural neurons and
nodes connected. Each of these nodes performs one
simple mathematical purpose, typically an linear
function that is responsible for the extraction
and mapping of information.
There are three layers to a deep neural network
the input layer, hidden layers, and the output
layer.
Data is fed to the layer of input.
Each node within the input layer takes in the
data before passing it to the layer next, i.e.
those hidden layers. The hidden layers are able
to remove features from the input layer and
transform it with the linear functions.
These layers are known as hidden layers since
they are not able to determine the parameter
(weights as well as biases) within each node are
unidentified. These layers use random variables
to alter data. Each produces different results.
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Example of Deep Learning
Virtual assistants
Do you have Alexa, Cortana, or Siri? Virtual
assistants that are popular use deep learning to
learn the terms and languages that people use
when they interact with them. This means that
they are more proficient in providing the
information required.
Driverless vehicles
Automobiles that are autonomous have already
appeared on the roads. Deep learning algorithms
are able to identify whether the object that is
on the roadway is actually a bag or another
vehicle or a kid and act in a manner that is
appropriate.
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Example of Deep Learning
Facial recognition
Facial recognition plays a crucial function for
everything, from tag individuals on social media
to vital security measures. Deep learning permits
algorithms to perform with precision despite
changes in appearance like beards, hairstyles or
even poor lighting.
Medical science
Human genomes comprise around 3 billion DNA base
pair of the chromosomes. The machine learning
process is helping researchers and medical
professionals , to create individualized
medicines, detect cancer, and is currently
undergoing studies and application for medical
and pharmaceutical uses.
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Careers in deep learning
In all aspects in machine-learning and AI, the
careers that deal with deep learning have been
expanding exponentially. Deep learning can
provide companies and organizations systems the
ability to make rapid progress that are complex
and require a lot of explanation. Data Engineers
specialize in deep learning and design the
computational strategies needed by researchers to
push the limits of the field. Data Engineers
often work in particular areas of expertise and
have a mix of abilities across different research
projects.
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