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Li Yang (simplified Chinese: 李 阳; traditional Chinese: 李陽; pinyin: Lǐ Yáng; born 1969 in Changzhou, Jiangsu) is a Chinese educator and language instructor. He is the creator of Crazy English, an unorthodox method of teaching English. He claimed to have taught English to more than 20 million people in a decade.
Competency-based learning or competency-based education is a framework for teaching and assessment of learning. It is also described as a type of education based on predetermined "competencies," which focuses on outcomes and real-world performance. [1]
Computer-supported collaborative learning (CSCL) is a pedagogical approach wherein learning takes place via social interaction using a computer or through the Internet. This kind of learning is characterized by the sharing and construction of knowledge among participants using technology as their primary means of communication or as a common resource. [1]
Luring Shadows is a silent 1920 American religious mystery film directed by Joseph Levering and written by Oscar E. Goebel and Condé B. Pallen. [1] It was produced by the now defunct Catholic Art Association (not to be mistaken for the current Catholic Art Association ) and Inter-Ocean Film Corporation, and stars Aida Horton .
Credited as Yang Sze 1979: Bolo (a.k.a. Bolo the Brute) Bolo: Credited as Yang Sze 1978: Enter the Game of Death (a.k.a. Cross Hands Martial Arts or The King of Kung Fu) Yang See 1978: Enter Three Dragons: Bolo 1978: Bruce Li in New Guinea: Unknown 1978: Amsterdam Connection: Louie "Big Louie" Credited as Yang Sze 1978: Black Belt Jones 2 (a.k ...
Ensemble learning, including both regression and classification tasks, can be explained using a geometric framework. [15] Within this framework, the output of each individual classifier or regressor for the entire dataset can be viewed as a point in a multi-dimensional space.
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.
Learning to rank [1] or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement learning, in the construction of ranking models for information retrieval systems. [2]