Reinforcement Learning in AI

Reinforcement Learning in AI Reinforcement LearningMarkov’s Decision ProcessQ leaningGreedy Decision MakingNIM GameNIM Game Implementation with Python Reinforcement Learning Reinforcement Learning is about learning from experience, where agents are given a set of...

Unsupervised Learning in AI

Unsupervised Learning in AI Unsupervised LearningIntroductionClusteringComparison between Supervised, Unsupervised, and Reinforcement Learning. Unsupervised Learning This is the third major category of Machine Learning. Unsupervised learning happens when we have data...

Information Retrieval

Information Retrieval: In order to analyze and categorize the text, we’d like to be able to figure out information about the text, some meaning about the text as well. And, to be able to take data in the text form and retrieve information from it, this task is...

Natural Language Processing

Natural Language Processing in AI Topics Covered in Language Module Natural Language ProcessingSyntax and SemanticsContext-Free GrammarNLTKN-gramsTokenizationBag of WordsNaïve Bayes In language, we will cover how Artificial Intelligence is used to process human...

Neural Networks

Neural Networks are one of the most popular techniques and tools in Machine learning. Neural Networks were inspired by the human brain as early as in the 1940s. Researchers studied the neuroscience and researched about the working of the human brain i.e. how the human...

Gradient Descent

Gradient Descent When training a neural network, an algorithm is used to minimize the loss. This algorithm is called as Gradient Descent. And loss refers to the incorrect outputs given by the hypothesis function. The Gradient is like a slope, which gives the direction...

Probabilistic Reasoning

Probabilistic Reasoning Probabilistic Reasoning is the study of building network models which can reason under uncertainty, following the principles of probability theory. Bayesian Networks Bayesian network is a data structure which is used to represent the...

Dynamic Bayesian Networks

DBN is a temporary network model that is used to relate variables to each other for adjacent time steps. Each part of a Dynamic Bayesian Network can have any number of Xivariables for states representation, and evidence variables Et. A DBN is a type of Bayesian...

Utility Functions in Artificial Intelligence

The agents use the utility theory for making decisions. It is the mapping from lotteries to the real numbers. An agent is supposed to have various preferences and can choose the one which best fits his necessity. Utility scales and Utility assessments To help an agent...

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