Neural Network Important Notes | Artificial Intelligence (AI) for Class 9 PDF Download

Algorithm

An algorithm is a set of instructions used in machine learning that allows a computer programme to mimic how a human learns to classify certain types of data.

Supervised Learning 

Supervised learning is a method of developing artificial intelligence that involves training a computer algorithm on input data that has been labeled for a certain output.

Example of Supervised Learning
You obtain a set of photographs with descriptions of what’s on them, and then you train a model to detect fresh photos.

Unsupervised Learning

The use of artificial intelligence (AI) systems to find patterns in data sets including data points that are neither categorized nor labeled is known as unsupervised learning.

Example of Unsupervised Learning
Assume the unsupervised learning algorithm is given an input dataset with photographs of various cats and dogs. The algorithm is never trained on the given dataset, therefore it has no knowledge what the dataset’s characteristics are.

Reinforcement Learning

“An intelligent agent interacts with the environment and learns to operate within that environment through reinforcement learning.”

Example of Reinforcement Learning

Neural Network

• Warren McCulloch and Walter Pitts proposed neural networks for the first time in 1944.
• A neural network is an artificial intelligence strategy for teaching computers to analyze data in the same way that the human brain does. Deep learning is a form of machine learning technique that employs interconnected nodes or neurons in a layered structure to mimic the human brain. It develops an adaptive framework that allows computers to learn from their errors and continuously improve.
  

Some of the features of a Neural Network are listed below:

• The human brain and nervous system are used to model neural network systems.
• They can automatically extract features without the programmer’s input.
• Every node in a neural network is a machine learning algorithm.
• It comes in handy while working on difficulties with a large data set.

Neural Networks Vs Human Nervous System

• Size: The human brain contains 86 billion neurons and over 100 trillion connections that transmit electrical information throughout the body. The number of neurons in the artificial neural network is far lower.
• Memory: The primary distinction is that humans forget, whereas neural networks do not. A neural network that has been properly trained.
• Energy Consumption: The biological brain uses roughly 20% of the total energy consumed by the human body. Artificial constructions can’t even come close to matching the efficiency level of a biological brain, which operates on roughly 20 watts.