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Diagram of a Federated Learning protocol with smartphones training a global AI model. Federated learning (also known as collaborative learning) is a machine learning technique in a setting where multiple entities (often called clients) collaboratively train a model while keeping their data decentralized, [1] rather than centrally stored.
GDScript, a scripting language very similar to Python, built-in to the Godot game engine. [238] Go is designed for the "speed of working in a dynamic language like Python" [239] and shares the same syntax for slicing arrays. Groovy was motivated by the desire to bring the Python design philosophy to Java. [240]
Deeplearning4j can be used via multiple API languages including Java, Scala, Python, Clojure and Kotlin. Its Scala API is called ScalNet. [31] Keras serves as its Python API. [32] And its Clojure wrapper is known as DL4CLJ. [33] The core languages performing the large-scale mathematical operations necessary for deep learning are C, C++ and CUDA C.
Moreover, numerous graph-related applications are found to be closely related to the heterophily problem, e.g. graph fraud/anomaly detection, graph adversarial attacks and robustness, privacy, federated learning and point cloud segmentation, graph clustering, recommender systems, generative models, link prediction, graph classification and ...
While research in single-agent reinforcement learning is concerned with finding the algorithm that gets the biggest number of points for one agent, research in multi-agent reinforcement learning evaluates and quantifies social metrics, such as cooperation, [2] reciprocity, [3] equity, [4] social influence, [5] language [6] and discrimination. [7]
DeepMind is known to have trained the program on over 170,000 proteins from the Protein Data Bank, a public repository of protein sequences and structures.The program uses a form of attention network, a deep learning technique that focuses on having the AI identify parts of a larger problem, then piece it together to obtain the overall solution. [2]
The use of the terminology is in need of clarification. Machine learning is not confined to association rule mining, c.f. the body of work on symbolic ML and relational learning (the differences to deep learning being the choice of representation, localist logical rather than distributed, and the non-use of gradient-based learning algorithms).
A Tsetlin machine is a form of learning automaton collective for learning patterns using propositional logic. Ole-Christoffer Granmo created [1] and gave the method its name after Michael Lvovitch Tsetlin, who invented the Tsetlin automaton [2] and worked on Tsetlin automata collectives and games. [3]