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The relative reactivity of the hexafluorides of S, Se, and Te follows the order TeF 6 > SeF 6 > SF 6, the latter being completely inert toward hydrolysis until high temperatures. SeF 6 also resists hydrolysis. [3] The gas can be passed through 10% NaOH or KOH without change, but reacts with gaseous ammonia at 200 °C. [8]
Unlike a single large network that can be assigned to arbitrary tasks, each module in a modular network must be assigned a specific task and connected to other modules in specific ways by a designer. In the vision example, the brain evolved (rather than learned) to create the LGN. In some cases, the designer may choose to follow biological models.
High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to produce because of the large amount of time needed to label the data. Although they do not need to be labeled, high-quality datasets for unsupervised learning can also be difficult and costly to produce ...
The goal of learning in the self-organizing map is to cause different parts of the network to respond similarly to certain input patterns. This is partly motivated by how visual, auditory or other sensory information is handled in separate parts of the cerebral cortex in the human brain. [6] An illustration of the training of a self-organizing map.
In the later half of the 1990s, open, interactive, situated and networked views of learning were marginalised by educational institutions as they tended to develop or deploy content and practice through proprietary learning management systems (e.g. Blackboard Inc, WebCT), and collaborative work tools such as IBM Lotus Notes/Learning Space and ...
A Personal Learning Network (PLN) is an informal learning network that consists of the people a learner interacts with and derives knowledge from in a personal learning environment. In a PLN, a person makes a connection with another person with the specific intent that some type of learning will occur because of that connection.
A social learning network (SLN) is a type of social network that results from interaction between learners, teachers, and modules of learning. [1] The modules and actors who form the SLN are defined by the specific social learning process taking place. [2] The set of learners and the set of teachers in an SLN cannot be disjoint.
This can result in improved learning efficiency and prediction accuracy for the task-specific models, when compared to training the models separately. [1] [2] [3] Inherently, Multi-task learning is a multi-objective optimization problem having trade-offs between different tasks. [4] Early versions of MTL were called "hints". [5] [6]