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3. Natural Language Processing
Natural language processing (NLP) is the ability to process natural, human-created text. Neural
networks help computers gather insights and meaning from text data and documents. NLP has
several uses, such as:
• Automated virtual agents and chatbots.
• Automatic organisation and classification of written data.

• Business intelligence analysis of long-form documents like emails and forms.
• Indexing of key phrases that indicate sentiment, like positive and negative comments on social
media.
4. Recommendation Engines
Artificial neural networks can track user activity to develop personalised recommendations. They
can also analyse all user behaviour and discover new products or services that interest a specific
user. Intelligent Product Tagging (IPT) uses neural networks to automatically find and recommend
products relevant to the user’s social media activity. Consumers don’t have to hunt through online
catalogues to find a specific product from a social media image.

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.
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.

How Deep Learning Works?
Deep Learning 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.
Deep neural networks consist of multiple layers of interconnected
nodes, each building upon the previous layer to refine and
optimise the prediction or categorisation. This progression of

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