In computational complexity theory, NL (Nondeterministic Logarithmic-space) is the complexity class containing decision problems that can be solved by a nondeterministic Turing machine using a logarithmic amount of memory space. NL is a generalization of L, the class for logspace problems on a deterministic Turing machine. Since any deterministic Turing machine is also a nondeterministic Turing machine, we have that L is contained in NL. NL can be formally defined in terms of the computational resource nondeterministic space (or NSPACE) as NL = NSPACE(log n). Important results in complexity theory allow us to relate this complexity class with other classes, telling us about the relative power of the resources involved. Results in the field of algorithms, on the other hand, tell us which problems can be solved with this resource. Like much of complexity theory, many important questions about NL are still open (see Unsolved problems in computer science). Occasionally NL is referred to as RL due to its below; however, this name is more frequently used to refer to randomized logarithmic space, which is not known to equal NL. (Wikipedia).
The chaotic complexity of natural numbers | Data structures in Mathematics Math Foundations 175
This is a sobering and perhaps disorienting introduction to the fact that arithmetic with bigger numbers starts to look quite different from the familiar arithmetic that we do with the small numbers we are used to. The notion of complexity is key in our treatment of this. We talk about bot
From playlist Math Foundations
NLTK Corpora - Natural Language Processing With Python and NLTK p.9
Remember from the beginning, we talked about this term, "corpora." Again, corpora is just a body of texts. Generally, corpora are grouped by some sort of defining characteristic. NLTK is a massive toolkit for you. part of what they give you is a ton of highly valuable corpora to learn wi
From playlist NLTK with Python 3 for Natural Language Processing
Question-Answering in NLP (Extractive QA and Abstractive QA)
Search is a crucial functionality in many applications and companies globally. Whether in manufacturing, finance, healthcare, or *almost* any other industry, organizations have vast internal information and document repositories. Unfortunately, the scale of many companies' data means that
From playlist Question Answering in NLP Course
Algorithms Explained: Computational Complexity
An overview of computational complexity including the basics of big O notation and common time complexities with examples of each. Understanding computational complexity is vital to understanding algorithms and why certain constructions or implementations are better than others. Even if y
From playlist Algorithms Explained
Upper Bounds in Integer Complexity-CTNT 2020
Define ||n|| to be the complexity of n, which is the smallest number of 1s needed to write n using an arbitrary combination of addition and multiplication. For example, 6=(1+1)(1+1+1) shows that ||6|| is at most 5. We discuss recent results concerning upper and lower bounds for ||n||
From playlist CTNT 2020 - Conference Videos
Big O Notation: A Few Examples
This video is about Big O Notation: A Few Examples Time complexity is commonly estimated by counting the number of elementary operations (elementary operation = an operation that takes a fixed amount of time to preform) performed in the algorithm. Time complexity is classified by the nat
From playlist Computer Science and Software Engineering Theory with Briana
WordNet - Natural Language Processing With Python and NLTK p.10
Part of the NLTK Corpora is WordNet. I wouldn't totally classify WordNet as a Corpora, if anything it is really a giant Lexicon, but, either way, it is super useful. With WordNet we can do things like look up words and their meaning according to their parts of speech, we can find synonyms,
From playlist NLTK with Python 3 for Natural Language Processing
MIT 18.404J Theory of Computation, Fall 2020 Instructor: Michael Sipser View the complete course: https://ocw.mit.edu/18-404JF20 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP60_JNv2MmK3wkOt9syvfQWY Reviewed log space: NL is a subset of SPACE(log^2n) and NL is a subse
From playlist MIT 18.404J Theory of Computation, Fall 2020
MIT 18.404J Theory of Computation, Fall 2020 Instructor: Michael Sipser View the complete course: https://ocw.mit.edu/18-404JF20 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP60_JNv2MmK3wkOt9syvfQWY Quickly reviewed last lecture. Finished Immerman-Szelepcsenyi theorem
From playlist MIT 18.404J Theory of Computation, Fall 2020
19. Cell Trafficking and Protein Localization
MIT 7.016 Introductory Biology, Fall 2018 Instructor: Barbara Imperiali View the complete course: https://ocw.mit.edu/7-016F18 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63LmSVIVzy584-ZbjbJ-Y63 Professor Imperiali talks about trafficking, or how things get to where
From playlist MIT 7.016 Introductory Biology, Fall 2018
Why are complex numbers awesome? What are they and how are they useful? Free ebook http://bookboon.com/en/introduction-to-complex-numbers-ebook Test your understanding via a short quiz http://goo.gl/forms/3T2ZqTfgrL Make learning "complex" numbers easy through an interactive, fun and
From playlist Intro to Complex Numbers
IMT4306 Introduction to Research: functional programming.
IMT4306, Discussion on programming languages and programming paradigms.
From playlist Archive - Research in Mobile/Wearable Tech
Catherine Sulem: Soliton Resolution for Derivative NLS equation
Abstract: We consider the Derivative Nonlinear Schrödinger equation for general initial conditions in weighted Sobolev spaces that can support bright solitons (but exclude spectral singularities). We prove global wellposedness and give a full description of the long-time behavior of the s
From playlist Women at CIRM
Thomas KAPPELER - Analytic extensions of frequencies of integrable PDEs and applications
In form of a case study for the mKdV and the KdV2 equation we discuss a novel approach of representing frequencies of integrable PDEs which allows to extend them analytically to spaces of low regularity and to study their asymptotics. Applications include properties of the actions to frequ
From playlist Trimestre "Ondes Non linéaires" - June Conference
16. Interacting Particles Part 2
MIT 8.333 Statistical Mechanics I: Statistical Mechanics of Particles, Fall 2013 View the complete course: http://ocw.mit.edu/8-333F13 Instructor: Mehran Kardar This is the second of five lectures on Interacting Particles. License: Creative Commons BY-NC-SA More information at http://ocw
From playlist MIT 8.333 Statistical Mechanics I: Statistical Mechanics of Particles, Fall 2013
Hodge theory and derived categories of cubic fourfolds - Richard Thomas
Richard Thomas Imperial College London September 16, 2014 Cubic fourfolds behave in many ways like K3 surfaces. Certain cubics - conjecturally, the ones that are rational - have specific K3s associated to them geometrically. Hassett has studied cubics with K3s associated to them at the le
From playlist Mathematics
19. Games, Generalized Geography
MIT 18.404J Theory of Computation, Fall 2020 Instructor: Michael Sipser View the complete course: https://ocw.mit.edu/18-404JF20 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP60_JNv2MmK3wkOt9syvfQWY Quickly reviewed last lecture. Discussed a connection between games a
From playlist MIT 18.404J Theory of Computation, Fall 2020
An Exact Solution of the Macroscopic Fluctuation Theory by Kirone Mallick
DISCUSSION MEETING : STATISTICAL PHYSICS OF COMPLEX SYSTEMS ORGANIZERS : Sumedha (NISER, India), Abhishek Dhar (ICTS-TIFR, India), Satya Majumdar (University of Paris-Saclay, France), R Rajesh (IMSc, India), Sanjib Sabhapandit (RRI, India) and Tridib Sadhu (TIFR, India) DATE : 19 December
From playlist Statistical Physics of Complex Systems - 2022
ArrrrCamp 2014- Natural Language Processing with Ruby
By, Konstantin Tennhard Natural Language Processing (NLP) is the art and science of making sense of user-generated data. It is a combination of state-of-the-art computer science techniques and linguistics. Being able to analyze plain text data allows us to gain a lot of insights. Popular
From playlist ArrrrCamp 2014