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d. These four chits are to be given randomly to any four students in the Hidden Layer 2.
e. Each student of Hidden Layer 2 must write 2 different words, one on each chit, on two different
chits and pass it onto the output layer. Thus, the student in the output layer receives 12 chits in
all.

e. Each student of Hidden Layer 2 must write 2 different words, one on each chit, on two different
chits and pass it onto the output layer. Thus, the student in the output layer receives 12 chits in
all.

f. Finally, the student in the output layer has to understand the words on the 12 chits and make a
guess about the image shown to the input layer. The student will also write a summary about all
the received words to explain his/her reasoning.
g. Finally, the student of the output layer presents the summary to everyone and reveals the image
to all. If the identification matches the actual image, the whole network wins; else, they lose.

K Keyey TTermserms

u Machine Learning
Machine Learning, or ML, is a subset of AI that makes the system able to learn automatically from past
experience or past information.
u Deep Learning
Deep Learning is the most advanced form of AI that uses neural networks with several layers and self-
learning algorithms to analyse various forms of data.
u Anomaly Detection
Anomaly Detection involves identifying items, events, or observations differ significantly from the majority
of the observed data.
u Object Indentifition
Object Identification refers to a group of related computer vision tasks that involve identifying objects in
digital photographs.
u Object Localisation
Object Localization refers to identifying the location of one or more objects in an image and drawing a
bounding box around their extent.
u Data Feature
A data feature is an individual measurable property or characteristic of a dataset that is used as an input in
a machine learning model.
u Data Labelling

Data Labelling is the process of adding meaningful tags or labels to various features within a dataset.
u AI Modelling
AI Modelling refers to the process of creating algorithms and models that allow machines to mimic human
cognition and perform tasks involving understanding, reasoning, and learning from data.
u Supervised Learning
Supervised Learning is a category of machine learning that uses labelled datasets to train algorithms to
recognise patterns in the input data and predict outcomes.

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