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DEEP LEARNING

Deep Learning (DL) is a subset of machine learning, which is essentially a neural network with three or
more layers. These neural networks attempt to simulate the behaviour of the human brain, allowing it to
learn from large amounts of data. While a neural network with a single layer can still make approximate
predictions, additional hidden layers can help to optimise and refine it for accuracy.
Deep Learning drives many artificial intelligence applications and services that improve automation,
performing analytical and physical tasks without human intervention. Deep learning technology lies
behind everyday products and services, such as digital assistants, voice-enabled TV remotes, and credit

card fraud detection, as well as emerging technologies, such as self-driving cars.

Deep Learning Vs Machine Learning

Deep Learning, though a subset of Machine Learning, distinguishes itself from classical machine learning
by the type of data that it works with and the methods by which it learns.
Machine Learning algorithms leverage structured, labelled data to make predictions – meaning specific
features are defined from the input data for the model and organised into tables. This doesn’t necessarily
mean that it doesn’t use unstructured data; it just means that if it does, it generally goes through some
pre-processing to organise it into a structured format.

Deep Learning eliminates some of the data pre-processing that is typically involved with Machine
Learning. These algorithms can ingest and process unstructured data, like text and images, and automates
feature extraction, removing some of the dependency on human experts. For example, let’s say that
we had a set of photos of different pets, and we wanted to categorise by ‘cat’, ‘dog’, ‘hamster’, etc.
Deep Learning algorithms can determine which features, such as ears, are most important to distinguish
each animal from another. In Machine Learning, this hierarchy of features is established manually by a
human expert.
Then, through the processes of gradient descent and backpropagation, the Deep Learning algorithm
adjusts itself for accuracy, allowing it to make predictions about a new photo of an animal with increased
precision.

Machine Learning and Deep Learning models are capable of different types of learning as well, which
are usually categorised as Supervised learning, Unsupervised learning, and Reinforcement learning.
Supervised learning utilises labelled datasets to categorise or make predictions; this requires some kind
of human intervention to label the input data correctly.
In contrast, Unsupervised learning doesn’t require labeled datasets, and instead, it detects patterns in
the data, clustering them by any distinguishing characteristics.

Reinforcement learning is a process in which a model learns to become more accurate at performing an
action in an environment based on feedback in order to maximise the reward.

How Deep Learning Works?

Deep Learning neural networks, or artificial neural networks, attempt to mimic the human brain
through a combination of data inputs, weights, and biases. These elements work together to accurately
recognise, classify, and describe objects within the data.

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