Page 163 - AI Ver 1.0 Class 10
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A Neural Network is divided into multiple layers and each layer is further divided into several blocks called nodes.
Each node is responsible to do its task and pass on it to the next layer. First, we have the input layer which
receives the input in several different formats provided by the programmer and feeds it to the neural network. No
processing occurs in the input layer. The output layer predicts our final output. The output at each node is called
its activation or node value.
The layer present in-between input and output layers is called the hidden layers which perform most of the
computations required by our network. These layers are not visible to the user. Each node of the hidden layer has
its own machine learning algorithm which it executes on the data received by the input layer. The processed data
is then fed to the subsequent hidden layer. There can be multiple hidden layers depending upon the complexity
of the function to be performed by the model. The processed data by the hidden layers is passed onto the output
layer which then gives the final output to the user. No processing is done in the output layer.
Machine Learning Machine Learning
Data Answer
Algorithm + Hidden Rules Algorithm + Hidden Rules
Machine Learning Machine Learning
Data Answer
Algorithm + Hidden Rules Algorithm + Hidden Rules
Machine Learning Machine Learning Answer
Data
Algorithm + Hidden Rules Algorithm + Hidden Rules
Video Session
Watch this video,
Visit https://www.youtube.com/watch?v=bfmFfD2RIcg or scan the QR code and answer
the following question.
What did you learn from this video?
Important Features of Neural Networks
Some of the important features of Neural Network are:
• The model of the AI Neural network is based on the human neural network i.e., brain and Nervous system.
• They are designed in such a way that the information can be automatically extracted without the interaction of
the programmer.
• Every node of a neural network system is a machine Learning algorithm.
• It is best suited for AI models dealing with large data.
AI Project Cycle 161

