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Questions and Answers
What is an algorithm in the context of machine learning?
Which type of learning involves training a computer algorithm on input data that has been labeled for a certain output?
What is the main objective of unsupervised learning in artificial intelligence?
In reinforcement learning, how does an intelligent agent learn to operate within an environment?
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Who proposed neural networks for the first time in 1944?
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Which type of learning involves the use of AI systems to find patterns in data sets without labeled data points?
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What is the primary distinction between human memory and neural network memory?
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What aspect of a neural network's performance allows it to continuously improve?
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How does the energy consumption of a biological brain compare to that of artificial neural networks?
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Which characteristic distinguishes the size of the human brain from an artificial neural network?
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Why can't artificial constructions match the energy efficiency level of a biological brain?
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In terms of memory, what allows a well-trained neural network to differ from humans?
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Study Notes
Machine Learning Fundamentals
- An algorithm in the context of machine learning is a set of instructions that is used to train a computer to perform a specific task.
Types of Learning
- Supervised Learning: involves training a computer algorithm on input data that has been labeled for a certain output.
- Unsupervised Learning: the main objective is to identify patterns or structure in the data without a specific output in mind.
- Reinforcement Learning: an intelligent agent learns to operate within an environment by receiving rewards or penalties for its actions.
Neural Networks
- Origin of Neural Networks: proposed by Warren McCulloch and Walter Pitts in 1944.
- Unsupervised Learning: involves the use of AI systems to find patterns in data sets without labeled data points.
- Neural Network Memory: differs from human memory in that it is distributed and stored in the connections between neurons.
- Performance Improvement: a neural network's performance allows it to continuously improve through backpropagation and optimization of weights.
Biological Brain vs. Artificial Neural Networks
- Energy Consumption: the energy consumption of a biological brain is significantly lower than that of artificial neural networks.
- Size: the human brain is much larger and more complex than an artificial neural network.
- Energy Efficiency: artificial constructions cannot match the energy efficiency level of a biological brain due to the complexity and dynamic nature of biological neurons.
Neural Network Memory vs. Human Memory
- Storage: a well-trained neural network can store and recall vast amounts of information, but lacks the contextual understanding and emotional associations of human memory.
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Description
Test your knowledge on Neural Networks based on CBSE Skill Education Class 9 notes derived from the NCERT textbook 'Artificial Intelligence (417)'. This quiz covers topics like algorithms, supervised learning, and more.
