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Deep Learning

Deep learning is a subset of machine learning that focuses on using neural networks with many layers to analyse and
learn from large amounts of data. It teaches computers to do what humans do naturally. It is the key technology used
in voice controlled devices such as driverless cars, mobile phones, tablets, etc. In deep learning, a machine is trained
with a huge amount of data, which helps it to train itself on the data. Such machines are intelligent enough to develop
algorithms for themselves.
Here are some key points about deep learning:
u The structure of deep learning models is inspired by the neurons and connections in the human brain.
u Artificial Neural Networks (ANNs), or Neural networks, are the core of machine learning.
u These networks consist of node layers, including an input layer, one or more hidden layers, and an output layer.
u If the output of any node exceeds a certain threshold, the node is activated and sends data to the next layer.
u When the network has more than three layers, including the input and output layers, it is called a Deep Neural
Network (DNN).

Multiple hidden layers
Input layer
Output layer

Difference in desired
values

Backpropagation output layer

BRAINY In 2016, Google’s AlphaGo beat Go master Lee Sedol utilising deep learning neural networks
FACT most closely approaching human thought.

Applications of Deep Learning
Some applications of deep learning are as follows:

u Automated driving: Deep learning is used to spot stoplights and traffic signals and also to detect pedestrians, reducing
the incidence of accidents.
u Aerospace and defence: Identifying objects from satellites and locate safe and unsafe zones for troops is another
area where deep learning is playing a major role.
u Medical research: Cancer researchers use deep learning to automatically detect cancer cells.
u Industrial Automation: Deep learning helps detect when people or objects are within an unsafe distance from the
heavy machines thereby ensuring their safety.

Machine Learning

Machine learning refers to the field of artificial intelligence where computers are trained to learn from data and make
decisions or predictions. It encompasses supervised learning (using labelled data), unsupervised learning (finding
patterns in unlabelled data), and reinforcement learning (learning through trial and error).

Machine learning is widely applied in areas such as image recognition, natural language processing, and autonomous

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