Deep learning is a subset of machine learning where artificial
neural networks, algorithms inspired by the human brain, learn from large
amounts of data. Deep learning is the one category of machine
learning that emphasizes training the computer about the
basic instincts of human beings. It is a prime technology behind the concept of
virtual assistants, facial recognition, driverless cars, etc. The working of
deep learning involves training the data and learning from the experiences.
comprising
many layers, drive deep learning. Deep Neural Networks (DNNs) are such types of
networks where each layer can perform complex operations such as representation
and abstraction that make sense of images, sound, and text. Considered the fastest-growing field in machine learning, deep learning represents a truly
disruptive digital technology, and it is being used by increasingly more
companies to create new business models.
The working structure of Deep Learning:
Deep learning systems require large amounts of data to return
accurate results; accordingly, information is fed as huge data sets. When
processing the data,
artificial neural networks are able to classify data with the answers received
from a series of binary true or false questions involving highly complex
mathematical calculations. For example, a facial recognition program works by
learning to detect and recognize edges and lines of faces, then more
significant parts of the faces, and, finally, the overall representations of
faces. Over time, the program trains itself, and the probability of correct
answers increases. In this case, the facial recognition program will accurately
identify faces with time.
Let’s say the goal is to have a neural network recognize photos that contain a dog. All dogs don’t look exactly alike – consider a Rottweiler and a Poodle, for instance. Furthermore, photos show dogs at different angles and with varying amounts of light and shadow. So, a training set of images must be compiled, including many examples of dog faces which any person would label as “dog,” and pictures of objects that aren’t dogs, labeled (as one might expect),
“Not dog.” The images, fed into the neural network, are converted
into data. These data move through the network, and various nodes assign
weights to different elements. The final output layer compiles the seemingly
disconnected information – furry, has a snout, has four legs, etc. – and
delivers the output: dog.
Now, this answer received from the neural network will be compared
to the human-generated label. If there is a match, then the output is
confirmed. If not, the neural network notes the error and adjusts the
weightings. The neural network tries to improve its dog-recognition skills by
repeatedly adjusting its weights over and over again. This training technique
is called supervised learning, which occurs even when the neural networks are
not explicitly told what "makes" a dog. They must recognize patterns
in data over time and learn on their own.
After learning what Deep Learning is, and understanding the
principles of its working, let's go a little back and see the rise of Deep
Learning.
Now, let
us take an example to understand it. Suppose we want to make a system that can
recognize the faces of different people in an image. If we solve this as a
typical machine learning problem, we will define facial features such as eyes,
nose, ears, etc., and then, the system will identify which features are
more important for which person on its own.
Now,
deep-learning takes this one step ahead. Deep learning automatically finds out
the features which are important for classification because of deep neural
networks, whereas in the case of Machine Learning we had to manually define
these features.
As shown in the image above Deep Learning works as follows:
At the lowest level, network fixates on patterns of local contrast
as important.
The following layer is then able to use those patterns of local
contrast to fixate on things that resemble eyes, noses, and mouths
Finally, the top layer is able to apply those facial features to
face templates.
A deep neural network is capable of composing more and more complex
features in each of its successive layers.
Have you ever wondered how Facebook automatically labels or
tags all the person present in an image uploaded by you? Well, Facebook
uses Deep Learning in a similar fashion as stated in the above
example. Now, you would have realized the capability of Deep Learning and
how it can outperform Machine Learning in those cases where we have very little
idea about all the features that can affect the outcome. Therefore, a Deep
network can overcome the drawback of Machine Learning by drawing
inferences from data set consisting of input data without proper
labeling.
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