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Artificial Intelligence UPSC NOTE

 Artificial intelligence (AI) and how machines learn

  • Artificial intelligence (AI) and machine learning are two powerful tools that are rapidly transforming our world.

  • Artificial intelligence is a broad field of computer science that deals with the creation of intelligent agents, which are systems that can reason, learn, and act autonomously.

  • Machine learning is a subfield of AI that focuses on the development of algorithms that can learn from data without being explicitly programmed.

  • Data Collection: Machine learning algorithms require a massive amount of data to learn from.

  • This data can come from various sources, including sensors, text files, images, and videos.

  • Data Preprocessing: The raw data is often cleaned and formatted before being fed into the machine learning model. 

  • This may involve removing irrelevant information, correcting errors, and ensuring consistency.

  • Model Training: The machine learning algorithm is trained on the prepared data

  • This involves repeatedly exposing the algorithm to the data and adjusting its internal parameters to improve its ability to identify patterns and make predictions.

  • Model Evaluation: Once trained, the model's performance is evaluated on a separate dataset to assess its accuracy and generalizability.

  • Model Deployment: If the model performs well, it can be deployed in real-world applications.

  • This could involve using the model to make predictions, classify data, or control a physical system.

  • Machine learning plays a crucial role in enabling AI systems to learn and improve over time

  • By continuously learning from new data, AI systems can become more accurate and sophisticated in their ability to perform tasks.

What is Artificial Neural Networks (ANN)

  • Artificial Neural Networks are a specific type of machine learning algorithm inspired by the structure and function of the human brain.

Structure:

  • Artificial Neurons: ANNs are built from interconnected nodes called artificial neurons.

  • These loosely mimic the biological neurons in our brains.

  • Layers: Artificial neurons are organized into layers:

    • Input Layer: Receives the initial data.

    • Hidden Layers: Perform the main computations and information processing. There can be one or more hidden layers.

    • Output Layer: Provides the final result or prediction.

Function:

  • Information Flow: Data enters the network through the input layer and propagates forward through the hidden layers

  • Each layer performs calculations on the received data and passes it on to the next layer.

  • Learning: During training, the connections between the neurons are adjusted based on the errors between the network's predictions and the actual results. 

  • This adjustment process, called backpropagation, allows the network to learn from the data and improve its accuracy over time.

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Learnerz IAS | Concept oriented UPSC Classes in Malayalam: Artificial Intelligence UPSC NOTE
Artificial Intelligence UPSC NOTE
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