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The significantly reorganized revised edition of the book (2023) [2] expands and modernizes the presented mathematical principles, computational methods, data science techniques, model-based machine learning and model-free artificial intelligence algorithms. The 14 chapters of the new edition start with an introduction and progressively build ...
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.
AlexNet is highly influential, resulting in much subsequent work in using CNNs for computer vision and using GPUs to accelerate deep learning. As of early 2025, the AlexNet paper has been cited over 168,000 times according to Google Scholar.
Temporal difference (TD) learning refers to a class of model-free reinforcement learning methods which learn by bootstrapping from the current estimate of the value function. These methods sample from the environment, like Monte Carlo methods , and perform updates based on current estimates, like dynamic programming methods.
Region-based Convolutional Neural Networks (R-CNN) are a family of machine learning models for computer vision, and specifically object detection and localization. [1] The original goal of R-CNN was to take an input image and produce a set of bounding boxes as output, where each bounding box contains an object and also the category (e.g. car or ...
Competitive learning; Compositional pattern-producing network; Computational cybernetics; Computational neurogenetic modeling; Confabulation (neural networks) Connectionist temporal classification; Contrastive Hebbian learning; Contrastive Language-Image Pre-training; Convolutional deep belief network; Convolutional layer; COTSBot; Cover's theorem
Salakhutdinov was considering quitting the field of artificial intelligence when he met Hinton in 2004, but changed his mind after Hinton asked him to take part in a project focused on a new way to train artificial neural networks, which he dubbed "deep belief networks." This research made a large impact on the field of deep learning. [3]