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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 computations through the network is called
Forward Propagation. The input and output layers of a deep
neural network are called Visible layers. The Input layer is where

the deep learning model ingests the data for processing, and
the Output layer is where the final prediction or classification
is made.
Another process called Backpropagation uses algorithms, like gradient descent, to calculate errors in
predictions and then adjusts the weights and biases of the function by moving backwards through the
layers in an effort to train the model. Together, forward propagation and backpropagation allow a neural
network to make predictions and correct for any errors accordingly. Over time, the algorithm becomes
gradually more accurate.

Knowledge Discovery Subject Enrichment

Deep Learning requires a tremendous amount of computing power. High performance Graphical
Processing Units (GPUs) are ideal because they can handle a large volume of calculations across multiple
cores with copious memory available. However, managing multiple GPUs can create a large demand on
internal resources and are costly to scale.

DATA SCIENCE AND DATA SCIENTISTS

Data science combines math and statistics, specialised programming, advanced analytics, artificial
intelligence, and machine learning with specific subject matter expertise to uncover actionable insights
hidden in an organisation’s data. These insights can be used to guide decision-making and strategic
planning.

The accelerating volume of data sources and subsequently data has made data science one of the
fastest-growing fields across every industry. As a result, it is no surprise that the role of the data scientist
is one of the most coveted jobs world-wide. Organisations are increasingly reliant on them to interpret
data and provide actionable recommendations to improve business outcomes.

The Data Science Lifecycle

The Data Science Lifecycle involves various roles, tools, and processes, which enable analysts to glean
actionable insights. Typically, a data science project undergoes the following stages:
1. Data Ingestion: The lifecycle begins with the data collection, both raw structured and unstructured
data from all relevant sources using a variety of methods. These methods can include manual
entry, web scraping, and real-time streaming data from systems and devices. Data sources can
include structured data, such as customer data, along with unstructured data, such as log files,
video, audio, pictures, the Internet of Things (IoT), and social media.

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