What Is Deep Learning And How Does It Work?

1
What Is Deep Learning And How Does It Work?
2
Deep learning is a part of machine learning,
which involves the use of computer algorithms to
learn, improve and evolve on its own. Deep
learning may be considered similar to machine
learning. However, while machine learning works
with simple concepts, deep learning uses
artificial neural networks, which imitate the way
humans learn and think. 1. Evolution of Deep
Learning
3
Deep learning depends on neural networks to
evolve on its own. While limited computing powers
limited the sizes of neural networks in the past,
the more recent neural networks are bigger and
more sophisticated, thanks to technological
advancements in the area of big data analytics.
As such, these neural networks help computers to
learn, observe and react according to situations.
In some cases, deep learning helps computer react
faster than humans to complex situations and
solve complex problems with minimum or no human
intervention. 2. Working Methodology of Deep
Learning
4
Deep learning is driven by artificial neural
networks. Deep neural networks comprise of
different layers of individual networks, with
each layer performing different types of complex
operations like abstraction and representation,
which make sense of the text, sound, images,
etc. Neural networks are made up of multiple
layers of nodes, similar to neurons in the human
brain. The nodes between the individual layers
are connected and a neural network is considered
deeper if it has more layers in it. Just like a
neuron in the brain receives signals from nearby
neurons, the nodes in a network pass signals
between them and are assigned weights in the
process of being transferred between two
nodes. The heavier a node, the more the effect
it will assert on the next layers of nodes. The
final layer of nodes works in compiling the total
weighted inputs of all nodes and thus, produces
an appropriate output. 3. Requirements for Deep
Learning
5
Systems working with deep learning need powerful
hardware to process the vast data they work with.
The need for powerful hardware also arises due to
the complexity of the calculations the systems
work on. The training for deep learning
computations would take many weeks for even
experienced individuals. Like the hardware
requirements, deep learning systems need vast
data to work on and thus, provide accurate
results. The information required for these
systems is fed in the form of huge data sets. The
neural networks process the data and classify it
in groups based on answers received from several
true or false questions. These binary questions
will use complex. 4. Working Example of Deep
Learning Lets take the example of facial
recognition as part of a deep learning system. In
this case, the responsibility of the neural
network lies in recognizing photos that contain a
particular face, say the image of a cat. For,
there are different kinds of cats and different
photos show cats from a different angle with
different light angles. As such, a set of images
containing different kinds of cats in different
angles is compiled, labelling these images as
cats. Another set of images containing random
objects are also compiled in a separate list
labelled as not cats.
6
Both sets are then fed to the deep
neural network where they are converted into
data. As the data moves through the neural
network, the nodes in the layers assign different
weights to the individual elements. The final
layer then compiles all of these weights to
create connected information like has fur, has
four legs, has a snout, etc. to project the
image of a cat.
7
The answer thus generated is compared to the
label generated by humans. While matches are
confirmed as outputs, the network works on errors
by adjusting the weights repeatedly to improve
the cat recognition skill. The neural networks
undertake what is called supervised learning to
improvise on their own and use the learning to
recognize data patterns over time without further
instructions. 5. The Popularity of Deep
Learning While machine learning has been around
for quite a long time now, deep learning has only
gained popularity in recent years. Still, in the
early stages of development, deep learning is
slated to transform the society we live in. The
most significant use of deep learning systems is
being tested in self-driving cars. Using deep
neural networks, these systems are integrated
into cars and are trained to recognize traffic
lights, adjust speed accordingly and slow down or
stop when trying to avoid objects on the road.
8
We can also find the use of deep learning
systems in weather forecasting (evacuating people
ahead of storms), stock market predictions
(predicting when to sell or buy shares), medical
treatments (designing treatment plans based on
evidence from past diagnoses), etc. Article
Resource-https//www.eligocs.com/deep-learning/wh
at-is-deep-learning-and-how-does-it-work/