Tags
A tag is a keyword or label that categorizes your question with other, similar questions. Using the right tags makes it easier for others to find and answer your question.
For questions related to the concept of overfitting in machine learning, which can be loosely defined as the gap between the performance on the training set and the performance on the test set.
98 questions
For questions related to the hyper-parameters of AI models and algorithms, which are parameters that are set before the learning process begins. For example, the number of hidden layers in a feed-forw…
94 questions
For questions related to word embeddings, which are vector representations of words.
93 questions
For questions related to feedforward neural networks (FFNNs), which are also sometimes called multilayer perceptrons, but these two expressions may not always be interchangeable.
92 questions
For questions related to the convergence of AI algorithms.
92 questions
For questions about chat-bots. NOT for questions about how to program a chat-bot, as those kinds of questions are off-topic.
91 questions
91 questions
For questions related to the convolution operation in mathematics, convolutional neural networks, image processing and computer vision.
89 questions
For questions related to the book "Reinforcement Learning: An Introduction" (by Andrew Barto and Richard S. Sutton).
88 questions
For questions related to the architecture of AI models, e.g. the architecture of neural networks.
87 questions
For questions about rewards functions (e.g. in the context of reinforcement learning, which may be denoted as $R(s, a)$).
87 questions
For questions related to the task of image generation, which can be done, for example, with variational auto-encoders (VAEs) or generative adversarial networks (GANs).
86 questions
For questions related to the concept of function approximation. For example, questions that involve the use of a neural network (which is a function approximator) in the context of RL in order to appr…
86 questions
For questions related to GPT (which stands for Generative Pre-Training), which is a combination of transformers (proposed in "Attention is All You Need") and unsupervised pre-training for solving lang…
86 questions
For questions related to graph neural networks, which are artificial neural networks applied to graphs.
85 questions
For questions about the concept of weight (or parameter) of a machine learning model, such as a neural network or a linear regression model.
85 questions
For questions related to the family of models known as YOLO (which stands for "You Only Look Once"), which were proposed by Joseph Redmon et al. There are at least three YOLO models (versions 1, 2, an…
83 questions
For questions related to the concept of loss (or cost) in machine learning or other AI sub-fields.
82 questions
For questions related to the temporal-difference reinforcement learning (RL) algorithms, which is a class of model-free (that is, they do not use the transition and reward function of the MDP) RL algo…
81 questions
For questions related to BERT (which stands for Bidirectional Encoder Representations from Transformers), a language representation model introduced in the paper "BERT: Pre-training of Deep Bidirectio…
81 questions
For questions about embeddings (not necessarily just word embeddings, for which there is a specific tag) in the context of machine learning.
80 questions
For question about Multi Layer Perceptron model/architecture, its training and other related details and parameters associated with the model.
79 questions
For questions related to the Monte Carlo methods in reinforcement learning and other AI sub-fields. ("Monte Carlo" refers to random sampling of the search space.)
79 questions
For questions related to geometric deep learning, which is the application of deep learning techniques to non-Euclidean data (e.g. graphs and manifolds).
79 questions
For questions related to AI theory that relies on the knowledge of a distribution of probabilities across one or more dimensions affecting probability. Such a distribution may be in discrete buckets, …
79 questions
For questions related to computational learning theory (or, in short, learning theory), which is a research subfield of artificial intelligence devoted to studying the design and mathematical analysis…
78 questions
For questions related to transfer learning, a machine learning method that focuses on storing knowledge gained while solving one problem in order to apply this knowledge to a different but related pro…
77 questions
For questions related to policies (as defined in reinforcement learning or other AI sub-fields).
76 questions
For questions related to the neuroevolution technique called NeuroEvolution of Augmenting Topologies (NEAT), introduced in the paper "Evolving Neural Networks through Augmenting Topologies" (2002) by …
74 questions
For questions about OpenAI's gym library, which provides a set of APIs to access different types of environments to train reinforcement learning agents.
73 questions
For questions related to the concept of a language model, which is a probability distribution over sequences of words (for example, of a natural language, such as English).
73 questions
For questions related to the concept of feature extraction, which is a set of techniques used to derive or create features from the existing set of features. Feature extraction is different from featu…
72 questions
For questions related to DeepMind's AlphaZero, which is a computer program that can play Go, Chess, and Shogi. AlphaZero achieved, within 24 hours of training, a superhuman level of play in these thre…
71 questions
For questions related to off-policy reinforcement learning algorithms, which estimate a policy (the target policy) while using another policy (the behavior policy), during the learning process, which …
70 questions
Use for questions involving minimax and variants such as maximin. Applies both to algorithms and the minimax theorem.
70 questions