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Artificial Agent

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What is Artificial Agent Artificial intelligence is defined as study of Logical or rational  agents. A Logical  agent could be anything which makes decisions,take decision,  like a person, firm, machine, or software. It carries out an action with the best outcome after considering past  and current percepts(agent’s perceptual inputs at a given instance). The artificial agent perceiving environment through sensors and acting upon that environment through actuators. perceiving its environment through  sensors  and acting upon that environment through  actuators Note  : Every agent can perceive its own actions (but not always the effects) To understand the structure of Intelligent Agents, we should be familiar with  Architecture  and  Agent Program .  Architecture  is the machinery that the agent executes on. It is a device with sensors and actuators, for example : a robotic car, a camera, a PC.  Age...
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What is HTML

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        HTML stands for  H yper  T ext  M arkup  L anguage         HTML is the  standard markup  language for Web pages         HTML  elements  are the building blocks of HTML pages         HTML elements are represented by  <> tags HTML Attributes HTML elements can have  attributes Attributes provide  additional information  about the element Attributes come in name/value pairs like  charset="utf-8"   HTML Document Structure Below is a visualization of an HTML document (an HTML Page): <html> <head> <title>Page title</title> </head> <body> <h1>This is a heading</h1> <p>This is a paragraph.</p> <p>This is another paragraph.</p> </body> </html> Note:  Only the content...
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What is machine learning

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This article discusses the categories of machine learning problems, and terminologies used in the field of machine learning. Types of machine learning problems There are various ways to classify machine learning problems. Here, we discuss the most obvious ones. 1. On basis of the nature of the learning “signal” or “feedback” available to a learning system Supervised learning : The computer is presented with example inputs and their desired outputs, given by a “teacher”, and the goal is to learn a general rule that maps inputs to outputs. The training process continues until the model achieves a desired level of accuracy on the training data. Some real life examples are: Image Classification:  You train with images/labels. Then in the future you give a new image expecting that the computer will recognize the new object. Market Prediction/Regression:  You train the computer with historical market data and ask the computer to predict the ...
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