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Questions and Answers
What are the interconnected nodes in neural networks called?
What are the interconnected nodes in neural networks called?
Which algorithm is used for training AI models based on data?
Which algorithm is used for training AI models based on data?
What metrics are commonly used to evaluate machine learning models?
What metrics are commonly used to evaluate machine learning models?
Which type of learning uses input-output pairs for training?
Which type of learning uses input-output pairs for training?
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What is a common type of neural network that allows feedback loops?
What is a common type of neural network that allows feedback loops?
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What does computer vision focus on within AI?
What does computer vision focus on within AI?
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Which of the following is a task associated with computer vision?
Which of the following is a task associated with computer vision?
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What is the main focus of Natural Language Processing (NLP) in AI?
What is the main focus of Natural Language Processing (NLP) in AI?
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Which of the following is an application of Natural Language Processing?
Which of the following is an application of Natural Language Processing?
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Neural networks are machine learning models that mimic the structure and function of which organ?
Neural networks are machine learning models that mimic the structure and function of which organ?
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Which of these is NOT mentioned as a part of Neural Networks in the text?
Which of these is NOT mentioned as a part of Neural Networks in the text?
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Study Notes
Artificial Intelligence (Sub. Code 417): Sub-Topics Overview
The CBSE curriculum for the subject, "Artificial Intelligence" under Sub. Code 417, covers several important aspects related to AI. These include Computer Vision, Natural Language Processing, Neural Networks, and Machine Learning Algorithms. In this article, we'll delve into these topics, providing a comprehensive overview and insights into each domain.
Computer Vision
Computer vision is a field of study within AI that focuses on enabling computers to interpret and process visual information from the environment. This includes tasks such as object recognition, image segmentation, and tracking motion. Through the course, learners will gain knowledge about the principles behind computer vision and practical examples of its applications.
Natural Language Processing
Natural Language Processing (NLP) is another crucial aspect of AI that involves teaching computers to understand, interpret, and generate human languages. It plays a significant role in various applications, including virtual assistants, sentiment analysis, and automated customer service. Students will explore techniques used in NLP, such as tokenization, stemming, and Part-of-Speech (POS) tagging, as well as more advanced methods like deep learning and neural networks.
Neural Networks
Neural networks are a type of machine learning model that mimics the structure and function of the human brain. They are composed of layers of interconnected nodes, or "neurons," that process information and make predictions or decisions based on the input data. Topics covered in the curriculum might include types of neural networks (e.g., feedforward, recurrent), backpropagation, and training methods.
Machine Learning Algorithms
Machine learning algorithms are techniques used to train AI models to make predictions or decisions based on data. Students will learn about different types of algorithms, such as supervised and unsupervised learning, decision trees, k-nearest neighbors, and support vector machines. The curriculum will also cover the evaluation of machine learning models, including metrics such as accuracy, precision, and recall.
By studying these topics, learners will gain a solid understanding of the principles behind AI, as well as practical skills and knowledge that can be applied in various fields and future job opportunities.
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Description
Test your knowledge on key sub-topics of Artificial Intelligence covered under Sub. Code 417, including Computer Vision, Natural Language Processing, Neural Networks, and Machine Learning Algorithms. Explore concepts like object recognition, NLP techniques, neural network structures, and machine learning algorithms.
