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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 computer vision. [7]
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.
Coates and Ng note that certain variants of k-means behave similarly to sparse coding algorithms. [16] In a comparative evaluation of unsupervised feature learning methods, Coates, Lee and Ng found that k-means clustering with an appropriate transformation outperforms the more recently invented auto-encoders and RBMs on an image classification ...
In 100 shogi games against Elmo (World Computer Shogi Championship 27 summer 2017 tournament version with YaneuraOu 4.73 search), AlphaZero won 90 times, lost 8 times and drew twice. [11] As in the chess games, each program got one minute per move, and Elmo was given 64 threads and a hash size of 1 GB. [2]
Unsupervised learning is a framework in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled data. [1] Other frameworks in the spectrum of supervisions include weak- or semi-supervision , where a small portion of the data is tagged, and self-supervision .
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. Such algorithms cannot ...
1.0: February 2019: Predominantly spatial image upscaler, required specifically trained for each game integration, included in Battlefield V and Metro Exodus, among others [5] "1.9" (unofficial name) August 2019: DLSS 1.0 adapted for running on the CUDA shader cores instead of tensor cores, used for Control [8] [4] [16] 2.0: April 2020
[1] An MLP without any hidden layer is essentially just a linear model. With hidden layers and activation functions, however, nonlinearity is introduced into the model. [1] In typical machine learning practice, the weights and biases are initialized, then iteratively updated during training via backpropagation. [1]