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This testing data is given as an input to the newly created AI model and the output received is checked and
evaluated on the basis of:
• Accuracy
• Precision
• Recall
• F1 score
Neural Networks
Neural Networks form a base of Deep learning, a subfield of Machine learning where algorithms are inspired by
the structure of the human brain. Neural networks take in data, train themselves to recognise the patterns in this
data and then predict the outputs for a new set of similar data. The most impressive aspect of neural networks is
that once trained, they learn on their own just like human brains.
Why do we use Neural Networks?
Neural Networks are a series of algorithms used to recognise hidden patterns in raw data, cluster and classify
it, and continuously learn and improve. They are used in a variety of applications in stock markets, sales and
marketing trends, risk assessment and fraud detection. The main advantage is that the data features can be
extracted automatically by the machine without the input from the developer. Neural networks are primarily used
for solving problems with large datasets, like images.
Large Neural Network
Model Performance SmallNeural Network
Medium Neural Network
Traditional Machine Learning
Algorithms
Size of Data
To summarize the need to use neural networks:
• It can extract data features automatically without the input from the developer.
• It is fast and efficient way to solve problems with large datasets, such as images.
• It is essentially a system of machine learning algorithms to perform certain tasks.
• The larger neural networks tend to perform better with larger amounts of data whereas the traditional machine
learning algorithm stops improving after a certain saturation point.
Working of Neural Networks
Neural networks are made up of layers of neurons, just like the human brain that consists of millions of neurons.
These neurons are the core processing units of the network.
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