Recurrent neural networkA recurrent neural network (RNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. In contrast to uni-directional feedforward neural network, it is a bi-directional artificial neural network, meaning that it allows the output from some nodes to affect subsequent input to the same nodes. Their ability to use internal state (memory) to process arbitrary sequences of inputs makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition.
Language educationLanguage education – the process and practice of teaching a second or foreign language – is primarily a branch of applied linguistics, but can be an interdisciplinary field. There are four main learning categories for language education: communicative competencies, proficiencies, cross-cultural experiences, and multiple literacies. Increasing globalization has created a great need for people in the workforce who can communicate in multiple languages.
Language schoolA language school is a school where one studies a foreign language. Classes at a language school are usually geared towards, for example, communicative competence in a foreign language. Language learning in such schools typically supplements formal education or existing knowledge of a foreign language. Students vary widely by age, educational background, work experience. They usually have the possibility of selecting a specific course according to their language proficiency.
Deep learningDeep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.
English as a second or foreign languageEnglish as a second or foreign language is the use of English by speakers with different native languages. Language education for people learning English may be known as English as a foreign language (EFL), English as a second language (ESL), English for speakers of other languages (ESOL), English as an additional language (EAL), or English as a New Language (ENL). The aspect in which EFL is taught is referred to as teaching English as a foreign language (TEFL), teaching English as a second language (TESL) or teaching English to speakers of other languages (TESOL).
Foreign languageA foreign language is a language that is not an official language of, nor typically spoken in, a specific country. Native speakers from that country usually need to acquire it through conscious learning, such as through language lessons at school, self-teaching, or attending language courses. A foreign language might be learned as a second language; however, there is a distinction between the two terms. A second language refers to a language that plays a significant role in the region where the speaker lives, whether for communication, education, business, or governance.
Teaching English as a second or foreign languageTeaching English as a foreign language (TEFL), Teaching English as a second language (TESL) or Teaching English to speakers of other languages (TESOL) are terms that refer to teaching English to students whose first language is not English. The terms TEFL, TESL, and TESOL distinguish between a class's location and student population. TEFL describes English language programs that occur in countries where English is not the primary language. TEFL programs may be taught at a language school or with a tutor.
Feedforward neural networkA feedforward neural network (FNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. Its flow is uni-directional, meaning that the information in the model flows in only one direction—forward—from the input nodes, through the hidden nodes (if any) and to the output nodes, without any cycles or loops, in contrast to recurrent neural networks, which have a bi-directional flow.
Types of artificial neural networksThere are many types of artificial neural networks (ANN). Artificial neural networks are computational models inspired by biological neural networks, and are used to approximate functions that are generally unknown. Particularly, they are inspired by the behaviour of neurons and the electrical signals they convey between input (such as from the eyes or nerve endings in the hand), processing, and output from the brain (such as reacting to light, touch, or heat). The way neurons semantically communicate is an area of ongoing research.
Residual neural networkA Residual Neural Network (a.k.a. Residual Network, ResNet) is a deep learning model in which the weight layers learn residual functions with reference to the layer inputs. A Residual Network is a network with skip connections that perform identity mappings, merged with the layer outputs by addition. It behaves like a Highway Network whose gates are opened through strongly positive bias weights. This enables deep learning models with tens or hundreds of layers to train easily and approach better accuracy when going deeper.
