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AIMA gives detailed information about the working of algorithms in AI. The book's chapters span from classical AI topics like searching algorithms and first-order logic, propositional logic and probabilistic reasoning to advanced topics such as multi-agent systems, constraint satisfaction problems, optimization problems, artificial neural networks, deep learning, reinforcement learning, and ...
Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation learning. The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data.
An autoencoder consisting of an encoder and a decoder is a paradigm for deep learning architectures. An example is provided by Hinton and Salakhutdinov [ 24 ] where the encoder uses raw data (e.g., image) as input and produces feature or representation as output and the decoder uses the extracted feature from the encoder as input and ...
Mamba [a] is a deep learning architecture focused on sequence modeling. It was developed by researchers from Carnegie Mellon University and Princeton University to address some limitations of transformer models , especially in processing long sequences.
Imitation learning is a paradigm in reinforcement learning, where an agent learns to perform a task by supervised learning from expert demonstrations. It is also called learning from demonstration and apprenticeship learning .
Double-loop learning recognises that the way a problem is defined and solved can be a source of the problem. [1] This type of learning can be useful in organizational learning since it can drive creativity and innovation, going beyond adapting to change to anticipating or being ahead of change. [2]
In machine learning, a deep belief network (DBN) is a generative graphical model, or alternatively a class of deep neural network, composed of multiple layers of latent variables ("hidden units"), with connections between the layers but not between units within each layer. [1]
The problem of learning an optimal decision tree is known to be NP-complete under several aspects of optimality and even for simple concepts. [ 35 ] [ 36 ] Consequently, practical decision-tree learning algorithms are based on heuristics such as the greedy algorithm where locally optimal decisions are made at each node.