Εισάγετε μια λέξη ή φράση σε οποιαδήποτε γλώσσα 👆
Γλώσσα:
Μετάφραση και ανάλυση λέξεων από τεχνητή νοημοσύνη
Σε αυτήν τη σελίδα μπορείτε να λάβετε μια λεπτομερή ανάλυση μιας λέξης ή μιας φράσης, η οποία δημιουργήθηκε χρησιμοποιώντας το ChatGPT, την καλύτερη τεχνολογία τεχνητής νοημοσύνης μέχρι σήμερα:
- πώς χρησιμοποιείται η λέξη
- συχνότητα χρήσης
- χρησιμοποιείται πιο συχνά στον προφορικό ή γραπτό λόγο
- επιλογές μετάφρασης λέξεων
- παραδείγματα χρήσης (πολλές φράσεις με μετάφραση)
- ετυμολογία
Τι (ποιος) είναι L2 Syntactic Complexity Analyzer - ορισμός
L2 Syntactic Complexity Analyzer
L2 Syntactical Complexity Analyzer (L2SCA) developed by Xiaofei Lu at the Pennsylvania State University, is a computational tool which produces syntactic complexity indices of written English language texts. Along with Coh-Metrix, the L2SCA is one of the most extensively used computational tool to compute indices of second language writing development.
Computational complexity
MEASURE OF THE AMOUNT OF RESOURCES NEEDED TO RUN AN ALGORITHM OR SOLVE A COMPUTATIONAL PROBLEM
Asymptotic complexity; Computational Complexity; Bit complexity; Context of computational complexity; Complexity of computation (bit); Computational complexities
In computer science, the computational complexity or simply complexity of an algorithm is the amount of resources required to run it. Particular focus is given to computation time (generally measured by the number of needed elementary operations) and memory storage requirements.
complexity
PROFESSIONAL ESPORTS ORGANIZATION BASED IN THE UNITED STATES
Los Angeles Complexity; CompLexity Gaming; LA Complexity; Complexity LA; CompLexity; Team CompLexity; CoL.Black; CoL
<algorithm> The level in difficulty in solving mathematically
posed problems as measured by the time, number of steps or
arithmetic operations, or memory space required (called time
complexity, computational complexity, and space complexity,
respectively).
The interesting aspect is usually how complexity scales with
the size of the input (the "scalability"), where the size of
the input is described by some number N. Thus an algorithm
may have computational complexity O(N^2) (of the order of the
square of the size of the input), in which case if the input
doubles in size, the computation will take four times as many
steps. The ideal is a constant time algorithm (O(1)) or
failing that, O(N).
See also NP-complete.
(1994-10-20)
