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Artificial intelligence
Artificial neural networks
Deep learning
Diffusion generative models
Machine learning
Perceptron
Reinforcement learning
Support vector machines
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Artificial neural networks
→
Machine learning
applied in
Artificial neural networks with trainable parameters can serve as learning models; early fixed logical networks did not automatically have the ability to learn.
Deep learning
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Machine learning
specializes
Deep learning uses multiple layers of trainable representations to learn, making it a class of machine-learning methods.
Diffusion generative models
→
Machine learning
specializes
Diffusion generative models learn a reverse process from data and form a class of generative machine-learning methods.
Machine learning
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Artificial intelligence
specializes
Within the engineering scope of AI covered by this family, machine learning improves performance through data and experience; AI also includes approaches that do not center on learning.
Perceptron
→
Machine learning
applied in
The perceptron adjusts its weights through feedback from examples, illustrating the application of neural models to learning classification tasks.
Reinforcement learning
→
Machine learning
specializes
Reinforcement learning learns to make decisions from rewards received through interaction with an environment and is the branch of machine learning concerned with sequential decision-making.
Support vector machines
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Machine learning
specializes
Support vector machines are a supervised-learning method with an explicit margin criterion, representing a specific approach within machine learning.