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For other places with the same name, see Wellington Square. Rewley House on the south side of Wellington Square at the junction with St John Street (on the right). Wellington Square in the snow. Wellington Square is a garden square in central Oxford, England, a continuation northwards of St John Street. In the centre of the square is a small park, Wellington Square Gardens , owned by the University of Oxford. A bicycle route passes into Little Clarendon Street through the pedestrian area at the front of the University Offices in the north-east of the Square. The street name is used to refer metonymically to the central administration of the University of Oxford, [1] [2] which in 1975 moved from the Clarendon Building to new buildings with an address in the Square but built at that time, along with graduate student accommodation, along the adjacent Little Clarendon Street. The University's Department for Continuing Education is in the Square in Rewley House, which...

For similar terms, see Solfeggietto and Solfege (manga). In music, solfège ( UK: / ˈ s ɒ l f ɛ dʒ / , [1] US: / s ɒ l ˈ f ɛ ʒ / ; French:  [sɔlfɛʒ] ) or solfeggio ( / s ɒ l ˈ f ɛ dʒ i oʊ / ; Italian:  [solˈfeddʒo] ), also called sol-fa , solfa , solfeo , among many names, is a music education method used to teach aural skills, pitch and sight-reading of Western music. Solfège is a form of solmization, and though the two terms are sometimes used interchangeably, the systems used in other music cultures such as swara , durar mufaṣṣalāt and Jianpu are discussed in their respective articles. Syllables are assigned to the notes of the scale and enable the musician to audiate, or mentally hear, the pitches of a piece of music which he or she is seeing for the first time and then to sing them aloud. Through the Renaissance (and much later in some shapenote publications) various interlocking 4, 5 and 6-note systems were employed to cover the octave. The tonic sol-fa method popularize...

.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty{ height:90px;width:728px;box-sizing:border-box; } 0 I'm trying to use the Keras embedding layer to create my own CBoW implementation to see how it works. I've generated outputs represented by a vector of the context word I'm searching for with size equal to my vocab. I've also generated inputs so that each context word has X many nearby words represented by their one-hot encoded vectors. So for example if my sentence is: "I ran over the fence to find my dog" using window size 2, I could generate the following input/output: [[over, the, to, find], fence] where 'fence' is my context word, 'over', 'the', 'to', ...