Convolutional neural networkConvolutional neural network (CNN) is a regularized type of feed-forward neural network that learns feature engineering by itself via filters (or kernel) optimization. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections. For example, for each neuron in the fully-connected layer 10,000 weights would be required for processing an image sized 100 × 100 pixels.
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.
Word orderIn linguistics, word order (also known as linear order) is the order of the syntactic constituents of a language. Word order typology studies it from a cross-linguistic perspective, and examines how different languages employ different orders. Correlations between orders found in different syntactic sub-domains are also of interest. The primary word orders that are of interest are the constituent order of a clause, namely the relative order of subject, object, and verb; the order of modifiers (adjectives, numerals, demonstratives, possessives, and adjuncts) in a noun phrase; the order of adverbials.
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.
V2 word orderIn syntax, verb-second (V2) word order is a sentence structure in which the finite verb of a sentence or a clause is placed in the clause's second position, so that the verb is preceded by a single word or group of words (a single constituent). Examples of V2 in English include (brackets indicating a single constituent): "Neither do I", "[Never in my life] have I seen such things" If English used V2 in all situations, then it would feature such sentences like: "[In school] learned I about animals", "[When she comes home from work] takes she a nap" V2 word order is common in the Germanic languages and is also found in Northeast Caucasian Ingush, Uto-Aztecan O'odham, and fragmentarily in Romance Sursilvan (a Rhaeto-Romansh variety) and Finno-Ugric Estonian.
Word embeddingIn natural language processing (NLP), a word embedding is a representation of a word. The embedding is used in text analysis. Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that words that are closer in the vector space are expected to be similar in meaning. Word embeddings can be obtained using language modeling and feature learning techniques, where words or phrases from the vocabulary are mapped to vectors of real numbers.
Object–subject–verb word orderIn linguistic typology, object–subject–verb (OSV) or object–agent–verb (OAV) is a classification of languages, based on whether the structure predominates in pragmatically neutral expressions. An example of this would be "Oranges Sam ate." OSV is rarely used in unmarked sentences, which use a normal word order without emphasis. Most languages that use OSV as their default word order come from the Amazon basin, such as Xavante, Jamamadi, Apurinã, Warao, Kayabí and Nadëb.
Verb–subject–object word orderIn linguistic typology, a verb–subject–object (VSO) language has its most typical sentences arrange their elements in that order, as in Ate Sam oranges (Sam ate oranges). VSO is the third-most common word order among the world's languages, after SOV (as in Hindi and Japanese) and SVO (as in English and Mandarin Chinese).
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.
Object–verb–subject word orderIn linguistic typology, object–verb–subject (OVS) or object–verb–agent (OVA) is a rare permutation of word order. OVS denotes the sequence object–verb–subject in unmarked expressions: Oranges ate Sam, Thorns have roses. The passive voice in English may appear to be in the OVS order, but that is not an accurate description. In an active voice sentence like Sam ate the oranges, the grammatical subject, Sam, is the agent and is acting on the patient, the oranges, which are the object of the verb, ate.
