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The document discusses methods for estimating the probability (P) that a sentence (e) is natural or grammatically correct using n-gram language models. It explains that n-gram models approximate P(e) by considering the probability of word sequences of length n rather than all preceding words. This helps address the problem of P(e) being estimated as 0 when e is not present in the training data. The document also covers smoothing techniques like linear interpolation and Witten-Bell smoothing that combine n-gram and (n-1)-gram probabilities to further address cases where n-gram probabilities are 0.

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Absolute Value

This document provides an introduction to absolute value, including definitions of key terms like positive and negative numbers. It explains that the absolute value of a number is the distance from zero, so the absolute value of positive numbers is the same as the number itself, while the absolute value of negative numbers is the positive version of that number. Examples are provided of absolute value equations with both positive and negative solutions. Real-world applications like banking debts are discussed.

Математик индукц

The document contains three mathematical proofs:
1. Using induction, it is proven that for any natural number n, the sum 2 + 4 + 6 + ... + 2n is equal to n(n+1).
2. Also by induction, it is shown that the sum 1/(3*5) + 1/(5*7) + ... + 1/(2n+1)(2n+3) equals n/(3(2n+3)) for any natural n.
3. Finally, induction is used to prove that for any natural number n, the expression 8n + 6 is divisible by 7.

Mathematics in the Modern World - GE3 - Set Theory

If you happen to like this powerpoint, you may contact me at flippedchannel@gmail.com
I offer some educational services like:
-powerpoint presentation maker
-grammarian
-content creator
-layout designer
Subscribe to our online platforms:
FlippED Channel (Youtube)
http://bit.ly/FlippEDChannel
LET in the NET (facebook)
http://bit.ly/LETndNET

Дараалал ба цуваа

Дараалал ба цуваа бүлэг сэдвийг 12р ангийн сурагчдад зориулан 3 цахим хичээл болгон оруулж байна
Цахим хичээл-1: Прогрессууд

Prime Time Power Point Presentation

The document defines key mathematical vocabulary terms including:
- Factors - numbers that are multiplied to obtain a product
- Multiples - the product of a number multiplied by another whole number
- Primes - numbers with exactly two factors, 1 and the number itself
- Composites - numbers with more than two factors
- Common multiples - multiples shared between two or more numbers
- Common factors - factors shared between two or more numbers
- Prime factorization - the unique product of prime numbers that results in the desired number according to the Fundamental Theorem of Arithmetic

Mathematical language-and-symbols-including-sets

This document provides an overview of mathematical language and symbols. It discusses how mathematics can be considered a language with its own precise, concise and powerful means of communicating ideas using symbols. Examples of common symbols are presented, as well as how English phrases can be translated into mathematical expressions. Sets are then introduced as collections of objects or elements that can be represented using set notation. Operations on sets like union, intersection and complement are defined along with examples.

Common Multiples and Common Factors

This document discusses determining common multiples and common factors of numbers. It explains that the common multiples of two numbers are the multiples that are shared between the two lists of all their individual multiples. The least common multiple is the smallest number that is a multiple of both numbers. It also explains that common factors are factors that two numbers have in common, and these can be determined by making lists of all the factors of each number and looking for the ones they share. A Venn diagram can also be used to visualize common factors between two numbers.

Rational numbers

The document discusses rational numbers. It defines rational numbers as numbers that can be expressed as fractions p/q where p and q are integers and q ≠ 0. The objectives are to identify different types of numbers, know properties of rational numbers, and represent and find rational numbers on the number line. It covers the properties of rational numbers including closure, commutativity, associativity, and distributivity. It also discusses concepts like reciprocal, negative, and finding rational numbers between two given rational numbers.

Absolute Value

This document provides an introduction to absolute value, including definitions of key terms like positive and negative numbers. It explains that the absolute value of a number is the distance from zero, so the absolute value of positive numbers is the same as the number itself, while the absolute value of negative numbers is the positive version of that number. Examples are provided of absolute value equations with both positive and negative solutions. Real-world applications like banking debts are discussed.

Математик индукц

The document contains three mathematical proofs:
1. Using induction, it is proven that for any natural number n, the sum 2 + 4 + 6 + ... + 2n is equal to n(n+1).
2. Also by induction, it is shown that the sum 1/(3*5) + 1/(5*7) + ... + 1/(2n+1)(2n+3) equals n/(3(2n+3)) for any natural n.
3. Finally, induction is used to prove that for any natural number n, the expression 8n + 6 is divisible by 7.

Mathematics in the Modern World - GE3 - Set Theory

If you happen to like this powerpoint, you may contact me at flippedchannel@gmail.com
I offer some educational services like:
-powerpoint presentation maker
-grammarian
-content creator
-layout designer
Subscribe to our online platforms:
FlippED Channel (Youtube)
http://bit.ly/FlippEDChannel
LET in the NET (facebook)
http://bit.ly/LETndNET

Дараалал ба цуваа

Дараалал ба цуваа бүлэг сэдвийг 12р ангийн сурагчдад зориулан 3 цахим хичээл болгон оруулж байна
Цахим хичээл-1: Прогрессууд

Prime Time Power Point Presentation

The document defines key mathematical vocabulary terms including:
- Factors - numbers that are multiplied to obtain a product
- Multiples - the product of a number multiplied by another whole number
- Primes - numbers with exactly two factors, 1 and the number itself
- Composites - numbers with more than two factors
- Common multiples - multiples shared between two or more numbers
- Common factors - factors shared between two or more numbers
- Prime factorization - the unique product of prime numbers that results in the desired number according to the Fundamental Theorem of Arithmetic

Mathematical language-and-symbols-including-sets

This document provides an overview of mathematical language and symbols. It discusses how mathematics can be considered a language with its own precise, concise and powerful means of communicating ideas using symbols. Examples of common symbols are presented, as well as how English phrases can be translated into mathematical expressions. Sets are then introduced as collections of objects or elements that can be represented using set notation. Operations on sets like union, intersection and complement are defined along with examples.

Common Multiples and Common Factors

This document discusses determining common multiples and common factors of numbers. It explains that the common multiples of two numbers are the multiples that are shared between the two lists of all their individual multiples. The least common multiple is the smallest number that is a multiple of both numbers. It also explains that common factors are factors that two numbers have in common, and these can be determined by making lists of all the factors of each number and looking for the ones they share. A Venn diagram can also be used to visualize common factors between two numbers.

Rational numbers

The document discusses rational numbers. It defines rational numbers as numbers that can be expressed as fractions p/q where p and q are integers and q ≠ 0. The objectives are to identify different types of numbers, know properties of rational numbers, and represent and find rational numbers on the number line. It covers the properties of rational numbers including closure, commutativity, associativity, and distributivity. It also discusses concepts like reciprocal, negative, and finding rational numbers between two given rational numbers.

Комплекс тоо цуврал хичээл-1

The document provides examples of performing arithmetic operations on complex numbers. It shows adding, subtracting, and multiplying complex numbers in the form of a + bi. Examples include combining terms with the same real and imaginary parts and distributing operations across terms. It also demonstrates dividing one complex number by another. The document concludes by stating example 18 builds upon the previous example 17.

Reducible equation to quadratic form

This document discusses different types of equations that can be reduced to quadratic form. It provides examples of each type:
1) Equations of the form ax4 - bx2 + c = 0 can be reduced to quadratic by substituting x2 = y.
2) Equations containing terms like apx + b/px can be reduced by substituting the x terms as y.
3) Reciprocal equations of the form ax2 + 1/x2 + bx + 1/x + c = 0 are reduced by substituting x - 1/x = y.
4) Exponential equations can be reduced by substituting a variable for the exponential term.
5) Equations

Addition and subtraction of rational expression

To add or subtract fractions with unlike denominators:
1. Find the least common denominator (LCD), which contains all prime factors of each denominator raised to the highest power.
2. Convert the fractions to equivalent fractions with the LCD as the denominator.
3. Perform the addition or subtraction on the numerators and write the sum or difference over the common denominator.

Integers

The document discusses integers and their properties. It defines integers as the set of numbers {..., -3, -2, -1, 0, 1, 2, 3, ...} which includes natural numbers, whole numbers, and their negatives. A number line is used to represent integers visually with negatives to the left of 0 and positives to the right. Rules for addition and subtraction of integers are provided, such as keeping the sign of the number with the greater magnitude. Multiplication and division of integers results in a positive answer if there is an even number of negative factors and negative if there is an odd number.

1.5 equations and solutions 1

This document provides examples and explanations for solving equations using mental math. It includes sample equations such as 15 - n = 4 and their step-by-step solutions. Students are guided through additional practice problems solving for variables in equations and determining if a given value satisfies the equation. The document aims to teach students how to set up and solve one-step equations using logic and arithmetic operations.

Maths solving equations

This document provides step-by-step instructions for solving various basic algebra equations, including equations with variables on both sides, equations involving fractions or decimals, and equations with variables being multiplied or divided. Learners are guided through solving for the variable in equations such as x+3=9, x/5=2.5, 3p=21, 2x+5=19, and 3(2x-1)=21.

How to Integrate an Equation | Jameel Academy

1. The document provides steps to integrate equations, beginning with trying basic integration formulas. If that does not work, methods like integration by substitution, parts, or trig substitutions are suggested.
2. As a last resort, the document advises adding numbers or factoring trinomials to manipulate the equation into a form that can be integrated using basic formulas or methods.
3. Examples are provided demonstrating each step, from integrating straightforward equations to more complex examples that require manipulation or advanced methods.

How to write equations &expressions

This document provides guidance on proper mathematical notation and grammar. It discusses:
- The standard formats for writing expressions, equations, and solving equations.
- How to write variables, coefficients, and terms properly.
- Acceptable ways to show multiplication in expressions.
- The importance of aligning equal signs when working with equations.
- Techniques for writing solutions clearly and concisely.
- How to avoid confusion between similar characters like x and ×.

Indices

1) The document provides instructions for solving various mathematical equations and expressions involving indices.
2) Examples include solving equations for specific values of x, expressing one term in terms of others, and solving simultaneous equations.
3) Key steps shown include isolating variables, applying inverse operations, and setting expressions equal to find solutions.

Absolute Value Notes

This document provides information about integers, absolute value, and opposites:
- Absolute value refers to the distance of a number from zero on the number line and is always positive. It is represented by vertical bars around a number.
- To evaluate expressions with absolute value, simplify values within the bars first before combining terms.
- The opposite of an absolute value expression is found by placing a negative sign before the absolute value bars.

Class Presentation Math 1

1) When multiplying integers with the same sign, the product is positive, but with different signs, the product is negative.
2) For exponents, if the base is in parentheses, you raise that number to the power. If the base is negative without parentheses, you raise the absolute value to the power and then make the answer negative.
3) The distributive property distributes the number being multiplied over terms in parentheses by multiplying each term individually and then combining like terms.

S 1

The document discusses solving absolute value equations. It defines absolute value as the distance of a number from zero, which is never negative. To solve an absolute value equation, isolate the absolute value term and make two equations by changing the non-absolute value side to positive and negative values, yielding two solutions.

Unit 4.6

The document discusses how to solve absolute value inequalities by determining if the distance between a variable and 0 is less than, greater than, or equal to a given value, and representing the solutions on a number line. Examples are provided to illustrate solving inequalities such as |x| < 3, |x| > 3, and |x| ≥ 6, explaining that the solutions lie between, beyond, or beyond and equal to certain values. Practice problems are then given for students to solve additional absolute value inequalities and graph the solutions.

Sequences

The document defines key concepts related to sequences:
- A sequence is a function with natural numbers as inputs and real numbers as outputs. Sequences can be bounded, meaning all terms fall within a certain range, or unbounded.
- Sequences can converge, meaning the terms get closer to a limit, or diverge, meaning the terms approach positive or negative infinity.
- A sequence is monotonically increasing if each term is greater than or equal to the previous one, and monotonically decreasing if each term is less than or equal to the previous one.
- A sequence converges to a value L if, given any small positive number ε, there exists an N such that all terms after N are within

Harkeerit&Kyra

An absolute value of a number is its distance from zero on the number line. To write an absolute value, use bars (e.g. |5| = 5). To solve an absolute value equation, separate it into two cases - one where the expression inside the bars is positive and one where it is negative. Then solve each case separately and check the solutions. Basic graphs of absolute value functions form a V-shape, with restrictions so the y-values are always greater than or equal to 0. Shifting or stretching transformations can be applied.

Introduction To Equations

The document discusses equations and solving equations. It defines an equation as a statement of equality between two or more quantities that always includes an equal sign. Equations can be true, false, or open. To solve an equation means to find the value of the variable that makes the statement true. The document provides examples of different types of equations and introduces the cover-up method for solving equations.

Finding the sum of a geometric sequence

Two sample problems on how to find the sum of a geometric sequence. One problem has a common ratio value that is less than 1, and the other has a common ratio value larger than 1.

3h. Pedagogy of Mathematics (Part II) - Algebra (Ex 3.8)

Pedagogy of Mathematics (Part II) - Algebra, Algebra, Maths, IX std Maths, Samacheerkalvi maths, II year B.Ed., Pedagogy, Mathematics, Factorization using synthetic division

The magic of vedic maths

The document discusses techniques from Vedic mathematics for performing calculations more easily and quickly in one's head. It provides examples of using vertical and crosswise multiplication to multiply two-digit numbers in a single line. This technique can be adapted for division, addition, subtraction and other operations. It also presents "tricks" for mentally multiplying or squaring numbers near multiples of 10, multiplying by 9 or 11, and squaring two-digit numbers ending in 5. The goal is to make calculations faster and more intuitive through Vedic mathematical formulas.

[Paper Introduction] Distant supervision for relation extraction without labe...

[Paper Introduction] Distant supervision for relation extraction without labe...NAIST Machine Translation Study Group

Distant supervision for relation extraction without labeled data
Mike Mintz, Steven Bills, Rion Snow, Dan Jurafsky
ACL 2009
I introduced this paper at NAIST Machine Translation Study Group.RNN-based Translation Models (Japanese)

RNNを用いた翻訳モデルの概説

The Main Concepts of Speech Recognition

The document provides an overview of the main concepts in speech recognition systems, including the lexicon, acoustic model, language model, and WFST decoder. It explains that the lexicon maps words to phone sequences, the acoustic model identifies pronunciations from audio features using deep neural networks, and the language model provides word probability distributions. It describes how the WFST decoder integrates these components by decoding speech as a path through a weighted finite state transducer to arrive at the most likely transcription.

Комплекс тоо цуврал хичээл-1

The document provides examples of performing arithmetic operations on complex numbers. It shows adding, subtracting, and multiplying complex numbers in the form of a + bi. Examples include combining terms with the same real and imaginary parts and distributing operations across terms. It also demonstrates dividing one complex number by another. The document concludes by stating example 18 builds upon the previous example 17.

Reducible equation to quadratic form

This document discusses different types of equations that can be reduced to quadratic form. It provides examples of each type:
1) Equations of the form ax4 - bx2 + c = 0 can be reduced to quadratic by substituting x2 = y.
2) Equations containing terms like apx + b/px can be reduced by substituting the x terms as y.
3) Reciprocal equations of the form ax2 + 1/x2 + bx + 1/x + c = 0 are reduced by substituting x - 1/x = y.
4) Exponential equations can be reduced by substituting a variable for the exponential term.
5) Equations

Addition and subtraction of rational expression

To add or subtract fractions with unlike denominators:
1. Find the least common denominator (LCD), which contains all prime factors of each denominator raised to the highest power.
2. Convert the fractions to equivalent fractions with the LCD as the denominator.
3. Perform the addition or subtraction on the numerators and write the sum or difference over the common denominator.

Integers

The document discusses integers and their properties. It defines integers as the set of numbers {..., -3, -2, -1, 0, 1, 2, 3, ...} which includes natural numbers, whole numbers, and their negatives. A number line is used to represent integers visually with negatives to the left of 0 and positives to the right. Rules for addition and subtraction of integers are provided, such as keeping the sign of the number with the greater magnitude. Multiplication and division of integers results in a positive answer if there is an even number of negative factors and negative if there is an odd number.

1.5 equations and solutions 1

This document provides examples and explanations for solving equations using mental math. It includes sample equations such as 15 - n = 4 and their step-by-step solutions. Students are guided through additional practice problems solving for variables in equations and determining if a given value satisfies the equation. The document aims to teach students how to set up and solve one-step equations using logic and arithmetic operations.

Maths solving equations

This document provides step-by-step instructions for solving various basic algebra equations, including equations with variables on both sides, equations involving fractions or decimals, and equations with variables being multiplied or divided. Learners are guided through solving for the variable in equations such as x+3=9, x/5=2.5, 3p=21, 2x+5=19, and 3(2x-1)=21.

How to Integrate an Equation | Jameel Academy

1. The document provides steps to integrate equations, beginning with trying basic integration formulas. If that does not work, methods like integration by substitution, parts, or trig substitutions are suggested.
2. As a last resort, the document advises adding numbers or factoring trinomials to manipulate the equation into a form that can be integrated using basic formulas or methods.
3. Examples are provided demonstrating each step, from integrating straightforward equations to more complex examples that require manipulation or advanced methods.

How to write equations &expressions

This document provides guidance on proper mathematical notation and grammar. It discusses:
- The standard formats for writing expressions, equations, and solving equations.
- How to write variables, coefficients, and terms properly.
- Acceptable ways to show multiplication in expressions.
- The importance of aligning equal signs when working with equations.
- Techniques for writing solutions clearly and concisely.
- How to avoid confusion between similar characters like x and ×.

Indices

1) The document provides instructions for solving various mathematical equations and expressions involving indices.
2) Examples include solving equations for specific values of x, expressing one term in terms of others, and solving simultaneous equations.
3) Key steps shown include isolating variables, applying inverse operations, and setting expressions equal to find solutions.

Absolute Value Notes

This document provides information about integers, absolute value, and opposites:
- Absolute value refers to the distance of a number from zero on the number line and is always positive. It is represented by vertical bars around a number.
- To evaluate expressions with absolute value, simplify values within the bars first before combining terms.
- The opposite of an absolute value expression is found by placing a negative sign before the absolute value bars.

Class Presentation Math 1

1) When multiplying integers with the same sign, the product is positive, but with different signs, the product is negative.
2) For exponents, if the base is in parentheses, you raise that number to the power. If the base is negative without parentheses, you raise the absolute value to the power and then make the answer negative.
3) The distributive property distributes the number being multiplied over terms in parentheses by multiplying each term individually and then combining like terms.

S 1

The document discusses solving absolute value equations. It defines absolute value as the distance of a number from zero, which is never negative. To solve an absolute value equation, isolate the absolute value term and make two equations by changing the non-absolute value side to positive and negative values, yielding two solutions.

Unit 4.6

The document discusses how to solve absolute value inequalities by determining if the distance between a variable and 0 is less than, greater than, or equal to a given value, and representing the solutions on a number line. Examples are provided to illustrate solving inequalities such as |x| < 3, |x| > 3, and |x| ≥ 6, explaining that the solutions lie between, beyond, or beyond and equal to certain values. Practice problems are then given for students to solve additional absolute value inequalities and graph the solutions.

Sequences

The document defines key concepts related to sequences:
- A sequence is a function with natural numbers as inputs and real numbers as outputs. Sequences can be bounded, meaning all terms fall within a certain range, or unbounded.
- Sequences can converge, meaning the terms get closer to a limit, or diverge, meaning the terms approach positive or negative infinity.
- A sequence is monotonically increasing if each term is greater than or equal to the previous one, and monotonically decreasing if each term is less than or equal to the previous one.
- A sequence converges to a value L if, given any small positive number ε, there exists an N such that all terms after N are within

Harkeerit&Kyra

An absolute value of a number is its distance from zero on the number line. To write an absolute value, use bars (e.g. |5| = 5). To solve an absolute value equation, separate it into two cases - one where the expression inside the bars is positive and one where it is negative. Then solve each case separately and check the solutions. Basic graphs of absolute value functions form a V-shape, with restrictions so the y-values are always greater than or equal to 0. Shifting or stretching transformations can be applied.

Introduction To Equations

The document discusses equations and solving equations. It defines an equation as a statement of equality between two or more quantities that always includes an equal sign. Equations can be true, false, or open. To solve an equation means to find the value of the variable that makes the statement true. The document provides examples of different types of equations and introduces the cover-up method for solving equations.

Finding the sum of a geometric sequence

Two sample problems on how to find the sum of a geometric sequence. One problem has a common ratio value that is less than 1, and the other has a common ratio value larger than 1.

3h. Pedagogy of Mathematics (Part II) - Algebra (Ex 3.8)

Pedagogy of Mathematics (Part II) - Algebra, Algebra, Maths, IX std Maths, Samacheerkalvi maths, II year B.Ed., Pedagogy, Mathematics, Factorization using synthetic division

The magic of vedic maths

The document discusses techniques from Vedic mathematics for performing calculations more easily and quickly in one's head. It provides examples of using vertical and crosswise multiplication to multiply two-digit numbers in a single line. This technique can be adapted for division, addition, subtraction and other operations. It also presents "tricks" for mentally multiplying or squaring numbers near multiples of 10, multiplying by 9 or 11, and squaring two-digit numbers ending in 5. The goal is to make calculations faster and more intuitive through Vedic mathematical formulas.

Комплекс тоо цуврал хичээл-1

Комплекс тоо цуврал хичээл-1

Reducible equation to quadratic form

Reducible equation to quadratic form

Addition and subtraction of rational expression

Addition and subtraction of rational expression

Integers

Integers

1.5 equations and solutions 1

1.5 equations and solutions 1

Maths solving equations

Maths solving equations

How to Integrate an Equation | Jameel Academy

How to Integrate an Equation | Jameel Academy

How to write equations &expressions

How to write equations &expressions

Indices

Indices

Absolute Value Notes

Absolute Value Notes

Class Presentation Math 1

Class Presentation Math 1

S 1

S 1

Unit 4.6

Unit 4.6

Sequences

Sequences

Harkeerit&Kyra

Harkeerit&Kyra

Introduction To Equations

Introduction To Equations

Finding the sum of a geometric sequence

Finding the sum of a geometric sequence

3h. Pedagogy of Mathematics (Part II) - Algebra (Ex 3.8)

3h. Pedagogy of Mathematics (Part II) - Algebra (Ex 3.8)

The magic of vedic maths

The magic of vedic maths

[Paper Introduction] Distant supervision for relation extraction without labe...

[Paper Introduction] Distant supervision for relation extraction without labe...NAIST Machine Translation Study Group

Distant supervision for relation extraction without labeled data
Mike Mintz, Steven Bills, Rion Snow, Dan Jurafsky
ACL 2009
I introduced this paper at NAIST Machine Translation Study Group.RNN-based Translation Models (Japanese)

RNNを用いた翻訳モデルの概説

The Main Concepts of Speech Recognition

The document provides an overview of the main concepts in speech recognition systems, including the lexicon, acoustic model, language model, and WFST decoder. It explains that the lexicon maps words to phone sequences, the acoustic model identifies pronunciations from audio features using deep neural networks, and the language model provides word probability distributions. It describes how the WFST decoder integrates these components by decoding speech as a path through a weighted finite state transducer to arrive at the most likely transcription.

[Paper Introduction] Efficient Lattice Rescoring Using Recurrent Neural Netwo...

[Paper Introduction] Efficient Lattice Rescoring Using Recurrent Neural Netwo...NAIST Machine Translation Study Group

Efficient Lattice Rescoring Using Recurrent Neural Network Language Models
X. Liu, Y. Wang, X. Chen, M. J. F. Gales & P. C. Woodland
ICASSP 2014
I introduced this paper at NAIST Machine Translation Study Group.Visual-Semantic Embeddings: some thoughts on Language

Language technology is rapidly evolving. A resurgence in the use of distributed semantic representations and word embeddings, combined with the rise of deep neural networks has led to new approaches and new state of the art results in many natural language processing tasks. One such exciting - and most recent - trend can be seen in multimodal approaches fusing techniques and models of natural language processing (NLP) with that of computer vision.
The talk is aimed at giving an overview of the NLP part of this trend. It will start with giving a short overview of the challenges in creating deep networks for language, as well as what makes for a “good” language models, and the specific requirements of semantic word spaces for multi-modal embeddings.

From A Neural Probalistic Language Model to Word2vec

뉴럴 랭귀지 모델
Word2vec

Using Deep Learning to do Real-Time Scoring in Practical Applications

http://www.meetup.com/SF-Bay-ACM/events/227480571/
(see also YouTube for a recording of the presentation)
The talk will cover a brief review of neural network basics and the following types of neural network deep learning:
* autocorrelational - unsupervised learning for extracting features. He will describe how additional layers build complexity in the feature extraction.
* convolutional - how to detect shift invariant patterns in various data sources. Horizontal shift invariant detection applies to signals like speech recognition or IoT data. Horizontal and vertical shift invariance applies to images or videos, for faces or self driving cars
* discuss details of applying deep net systems for continuous or real time scoring
* reinforcement learning or Q Learning - such as learning how to play Atari video games
* continuous space word models - such as word2vec, skipgram training, NLP understanding and translation

Word representations in vector space

- The document discusses neural word embeddings, which represent words as dense real-valued vectors in a continuous vector space. This allows words with similar meanings to have similar vector representations.
- It describes how neural network language models like skip-gram and CBOW can be used to efficiently learn these word embeddings from unlabeled text data in an unsupervised manner. Techniques like hierarchical softmax and negative sampling help reduce computational complexity.
- The learned word embeddings show meaningful syntactic and semantic relationships between words and allow performing analogy and similarity tasks without any supervision during training.

Language Model (D3L1 Deep Learning for Speech and Language UPC 2017)

Language Model (D3L1 Deep Learning for Speech and Language UPC 2017)Universitat Politècnica de Catalunya

https://telecombcn-dl.github.io/2017-dlsl/
Winter School on Deep Learning for Speech and Language. UPC BarcelonaTech ETSETB TelecomBCN.
The aim of this course is to train students in methods of deep learning for speech and language. Recurrent Neural Networks (RNN) will be presented and analyzed in detail to understand the potential of these state of the art tools for time series processing. Engineering tips and scalability issues will be addressed to solve tasks such as machine translation, speech recognition, speech synthesis or question answering. Hands-on sessions will provide development skills so that attendees can become competent in contemporary data analytics tools.End-to-end Speech Recognition with Recurrent Neural Networks (D3L6 Deep Learn...

End-to-end Speech Recognition with Recurrent Neural Networks (D3L6 Deep Learn...Universitat Politècnica de Catalunya

https://telecombcn-dl.github.io/2017-dlsl/
Winter School on Deep Learning for Speech and Language. UPC BarcelonaTech ETSETB TelecomBCN.
The aim of this course is to train students in methods of deep learning for speech and language. Recurrent Neural Networks (RNN) will be presented and analyzed in detail to understand the potential of these state of the art tools for time series processing. Engineering tips and scalability issues will be addressed to solve tasks such as machine translation, speech recognition, speech synthesis or question answering. Hands-on sessions will provide development skills so that attendees can become competent in contemporary data analytics tools.Deep Learning for Information Retrieval

This document provides an overview of deep learning for information retrieval. It begins with background on the speaker and discusses how the data landscape is changing with increasing amounts of diverse data types. It then introduces neural networks and how deep learning can learn hierarchical representations from data. Key aspects of deep learning that help with natural language processing tasks like word embeddings and modeling compositionality are discussed. Several influential papers that advanced word embeddings and recursive neural networks are also summarized.

코노랩스(최재훈 CTO)_AI Startup D.PARTY_20161020

AI기술을 활용한 스케쥴링 개인비서 'KONO'

Code로 이해하는 RNN

RNN을 python/numpy로 직접 구현한 code를 통하여, RNN을 이해. (BPTT 및 상태를 전달하는 과정 등) http://blog.naver.com/freepsw/220941652066 상세 설명 참고

20160203_마인즈랩_딥러닝세미나_05 딥러닝 자연어처리와 분류엔진 황이규박사

20160203_마인즈랩_딥러닝세미나_05 딥러닝 자연어처리와 분류엔진 황이규박사

[Paper Introduction] Distant supervision for relation extraction without labe...

[Paper Introduction] Distant supervision for relation extraction without labe...

RNN-based Translation Models (Japanese)

RNN-based Translation Models (Japanese)

The Main Concepts of Speech Recognition

The Main Concepts of Speech Recognition

[Paper Introduction] Efficient Lattice Rescoring Using Recurrent Neural Netwo...

[Paper Introduction] Efficient Lattice Rescoring Using Recurrent Neural Netwo...

Visual-Semantic Embeddings: some thoughts on Language

Visual-Semantic Embeddings: some thoughts on Language

From A Neural Probalistic Language Model to Word2vec

From A Neural Probalistic Language Model to Word2vec

Using Deep Learning to do Real-Time Scoring in Practical Applications

Using Deep Learning to do Real-Time Scoring in Practical Applications

Word representations in vector space

Word representations in vector space

Language Model (D3L1 Deep Learning for Speech and Language UPC 2017)

Language Model (D3L1 Deep Learning for Speech and Language UPC 2017)

End-to-end Speech Recognition with Recurrent Neural Networks (D3L6 Deep Learn...

End-to-end Speech Recognition with Recurrent Neural Networks (D3L6 Deep Learn...

Deep Learning for Information Retrieval

Deep Learning for Information Retrieval

코노랩스(최재훈 CTO)_AI Startup D.PARTY_20161020

코노랩스(최재훈 CTO)_AI Startup D.PARTY_20161020

Code로 이해하는 RNN

Code로 이해하는 RNN

20160203_마인즈랩_딥러닝세미나_05 딥러닝 자연어처리와 분류엔진 황이규박사

20160203_마인즈랩_딥러닝세미나_05 딥러닝 자연어처리와 분류엔진 황이규박사

Learning group em - 20171025 - copy

The EM algorithm is an iterative method to find maximum likelihood estimates of parameters in probabilistic models with latent variables. It has two steps: E-step, where expectations of the latent variables are computed based on current estimates, and M-step, where parameters are re-estimated to maximize the expected complete-data log-likelihood found in the E-step. As an example, the EM algorithm is applied to estimate the parameters of a Gaussian mixture model, where the latent variables indicate component membership of each data point.

P2-Chp3-SequencesAndSeries from pure maths 2.pptx

sequence and series using drfrost slides

Complete Residue Systems.pptx

The document defines a complete system of residues as a set of n integers modulo n that each correspond to the residues 0, 1, ..., n-1. It provides examples of complete systems of residues for n=7 and n=4, with sets of integers that each cover the possible residues when divided by n. The complete system of residues contains one and only one integer corresponding to each possible residue.

Rational function 11

- A rational expression is a ratio of two polynomial expressions, where the denominator is not equal to zero.
- To find the domain of a rational expression, set the denominator equal to zero and solve for values of x that make the denominator equal to zero. These values are excluded from the domain.
- To find the range, find the horizontal asymptote by comparing the degrees of the numerator and denominator. The range is all real numbers except the constant value of the horizontal asymptote.

Yr7-AlgebraicExpressions (1).pptx

This document provides an introduction to algebraic expressions and simplification. It discusses representing missing information with variables, examples of algebraic expressions, adding, subtracting, multiplying and dividing terms, and substituting values into expressions. Students are provided examples and interactive practice questions to help understand these algebraic concepts.

Binary Operations.pptx

The document defines various sets of numbers and binary operations. It then provides examples of binary operations on sets of numbers, such as addition and multiplication on sets of natural numbers, integers, rational numbers, real numbers, and complex numbers. The document also defines properties of binary operations such as commutativity, associativity, identity elements, and inverse elements. It provides problems and solutions showing examples of binary operations and verifying their properties.

IGCSEFM-FactorTheorem.pptx

1. The document discusses factorizing polynomials by using the remainder and factor theorems.
2. The remainder theorem states that the remainder when a polynomial f(x) is divided by x - a is f(a).
3. The factor theorem states that if f(a) = 0, then x - a is a factor of f(x).

Finding the general term (not constant)

The document provides an example of finding the general term of an arithmetic sequence. It shows setting up a table with the terms and differences, writing equations for the differences, and solving the equations to find that the general term is equal to (1/2)n(n+1).

SUEC 高中 Adv Maths (Quadratic Equation in One Variable)

Sources:
Visual - various maths sites (credits to original creator)
Questions - Dong Zong's Textbook
suitable for SUEC (Maths), SPM (Maths and Add Maths) too

Lecture5_Laplace_ODE.pdf

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Equations.pptx

The document discusses solving equations, including equations with unknowns on both sides and with brackets. It provides examples of solving various types of equations, such as equations with fractions or variables on both sides. Strategies for solving equations include collecting like terms, using the inverse operation to isolate the variable, and expanding any brackets before solving.

S1 z(def., prop., y operaciones)

This document discusses properties and operations of integer numbers. It defines integers as ordered pairs of natural numbers, and defines addition, subtraction, multiplication and division of integers. It proves several theorems about these operations, such as the addition of integers being well-defined regardless of the pairs used, and properties like commutativity and the existence of an identity element. It also classifies integers as positive, negative or zero based on the ordered pairs, and proves theorems about the results of operations between integers of different classes.

Sequence and series

The document defines sequences and series. A sequence is an ordered list of elements where order matters. Sequences can be finite or infinite. A series is the sum of the terms of a sequence. Sigma notation is used to represent the sum of terms in a sequence from one index to another. Examples show how to write out the terms of a sequence given a general term formula and how to express a series without sigma notation.

Комплекс тоо цуврал хичээл-2

This document discusses complex numbers and their properties in Mongolian. It defines the modulus of a complex number a + bi as √(a2 + b2). It provides examples of calculating the modulus of 3 + 2i and 4 - 5i. It then discusses the conjugate of a complex number a - bi. Other topics covered include complex number addition, multiplication, division, powers, and properties of polynomials with complex number coefficients. Worked examples are provided to illustrate these concepts and theorems.

Polynomial division

Polynomial division works similarly to ordinary long division, with the divisor polynomial having a lower degree than the dividend polynomial. The dividend is written as a multiple of the divisor plus a remainder, where the degree of the remainder is less than the degree of the original dividend. This process is repeated with the remainders as new dividends until the final remainder's degree is less than the divisor's degree. As an example, the document shows the polynomial 3x^3 + 2x^2 - 7x + 5 being divided by x + 3 through repeated use of this process.

Solving Poisson Equation using Conjugate Gradient Methodand its implementation

General description of solving poisson equation using conjugate gradient Methodand its implementation. i present this material on my lab meeting

PhyChem3_vector_matrix_mechanics.pptx

This document provides a review of mathematical concepts relevant to physical chemistry, including:
- Representation of 3D vectors in terms of basis vectors and components
- Definition of a basis and examples of standard and non-standard bases
- Representation of vectors and operators in terms of matrices
- Properties of bases, operators, and matrices like orthogonality, linearity, and order of operations
- Key matrix operations like transpose, adjoint, determinant, and representation of vectors and operators

ショアのアルゴリズム

The document describes Shor's algorithm, which is an algorithm for factorizing integers into prime factors. It works by finding the period of the powers of a random number a modulo the integer N being factorized. Specifically, it finds the smallest positive integer r such that ar = 1 (mod N). This r can then be used to factorize N into prime factors. The document provides an example of applying Shor's algorithm to factorize 35, using a = 3. It also describes representing the algorithm using quantum circuits.

Semana 10 numeros complejos i álgebra-uni ccesa007

The document discusses complex numbers. It defines the imaginary unit i as the number whose square is -1. It explains that any complex number z can be written in the form z = x + yi, where x is the real part and yi is the imaginary part. It discusses operations like addition, subtraction, conjugation and negation of complex numbers. Graphical representation of complex numbers in the complex plane is also covered.

WEEK 3.pdf

1. Diana, a Tinapa vendor, started selling atchara bottles at her store to pair with Tinapa. She sold 7 bottles the first week and added 7 more bottles each subsequent week.
2. To find how many weeks it will take to sell 105 bottles, we can model the situation as an arithmetic sequence and determine that the 15th term is 105.
3. Therefore, it will take 15 weeks to sell 105 atchara bottles.

Learning group em - 20171025 - copy

Learning group em - 20171025 - copy

P2-Chp3-SequencesAndSeries from pure maths 2.pptx

P2-Chp3-SequencesAndSeries from pure maths 2.pptx

Complete Residue Systems.pptx

Complete Residue Systems.pptx

Rational function 11

Rational function 11

Yr7-AlgebraicExpressions (1).pptx

Yr7-AlgebraicExpressions (1).pptx

Binary Operations.pptx

Binary Operations.pptx

IGCSEFM-FactorTheorem.pptx

IGCSEFM-FactorTheorem.pptx

Finding the general term (not constant)

Finding the general term (not constant)

SUEC 高中 Adv Maths (Quadratic Equation in One Variable)

SUEC 高中 Adv Maths (Quadratic Equation in One Variable)

Lecture5_Laplace_ODE.pdf

Lecture5_Laplace_ODE.pdf

Equations.pptx

Equations.pptx

S1 z(def., prop., y operaciones)

S1 z(def., prop., y operaciones)

Sequence and series

Sequence and series

Комплекс тоо цуврал хичээл-2

Комплекс тоо цуврал хичээл-2

Polynomial division

Polynomial division

Solving Poisson Equation using Conjugate Gradient Methodand its implementation

Solving Poisson Equation using Conjugate Gradient Methodand its implementation

PhyChem3_vector_matrix_mechanics.pptx

PhyChem3_vector_matrix_mechanics.pptx

ショアのアルゴリズム

ショアのアルゴリズム

Semana 10 numeros complejos i álgebra-uni ccesa007

Semana 10 numeros complejos i álgebra-uni ccesa007

WEEK 3.pdf

WEEK 3.pdf

On using monolingual corpora in neural machine translation

This document summarizes research on leveraging monolingual corpora to improve neural machine translation. The researchers investigated two methods ("shallow fusion" and "deep fusion") for integrating a language model trained on monolingual data into the decoder of an NMT system. They found that both methods led to improved translation performance, with gains of over 1 BLEU point for lower-resource language pairs and around 0.4 BLEU point for higher-resource pairs. The degree of improvement depended on how similar the domain of the monolingual data was to the translation domain, with greater benefits observed when the domains closely matched.

[Paper Introduction] Efficient top down btg parsing for machine translation p...

[Paper Introduction] Efficient top down btg parsing for machine translation p...NAIST Machine Translation Study Group

1) The document proposes an efficient top-down parsing algorithm for preordering source sentences in machine translation using bilexical grammar (BTG) trees. 2) Existing BTG-based preordering approaches are slow due to their use of CKY parsing and loss function calculations with time complexity of O(n^5). 3) The proposed approach uses an incremental top-down parsing algorithm with early updates and beam search, achieving time complexity of O(n^2) and making it 10-100 times faster than prior work. 4) Experimental results show the efficient approach provides better BLEU scores in machine translation compared to prior BTG preordering methods.[Paper Introduction] Translating into Morphologically Rich Languages with Syn...

[Paper Introduction] Translating into Morphologically Rich Languages with Syn...NAIST Machine Translation Study Group

Paper Introduction,
"Translating into Morphologically Rich Languages with Synthetic Phrases"
Victor Chahuneau, Eva Schlinger, Noah A. Smith, Chris Dyer (EMNLP2013)
[Paper Introduction] Supervised Phrase Table Triangulation with Neural Word E...

[Paper Introduction] Supervised Phrase Table Triangulation with Neural Word E...NAIST Machine Translation Study Group

Paper Introduction,
"Supervised Phrase Table Triangulation with Neural Word Embeddings for Low-Resource Languages"
Tomer Levinboim and David Chiang[Paper Introduction] Evaluating MT Systems with Second Language Proficiency T...

[Paper Introduction] Evaluating MT Systems with Second Language Proficiency T...NAIST Machine Translation Study Group

This study evaluates machine translation systems using second language proficiency tests to measure human performance on tasks using machine-translated texts. The researchers had 320 Japanese junior high students answer multiple-choice questions based on conversations translated by 4 systems - Google Translate, Yahoo Translate, and two human translations, one with and one without context. They found that considering context was important for accurate translations, as the system that included context performed better. Scores on the proficiency tests agreed somewhat with automatic evaluation metrics but captured additional aspects of translation quality. The tests also proved robust to differences between test-takers.[Paper Introduction] Bilingual word representations with monolingual quality ...

[Paper Introduction] Bilingual word representations with monolingual quality ...NAIST Machine Translation Study Group

1) The document discusses methods for creating bilingual word representations, which are vectors that represent words from two languages in a single vector space.
2) It presents an approach called Bilingual Skipgram that trains word representations by substituting words from one language to predict contexts in the other language.
3) Evaluation shows this approach achieves better performance on monolingual tasks compared to previous methods, while still performing well on cross-lingual tasks.[Paper Introduction] A Context-Aware Topic Model for Statistical Machine Tran...

[Paper Introduction] A Context-Aware Topic Model for Statistical Machine Tran...NAIST Machine Translation Study Group

The document presents a context-aware topic model (CATM) for statistical machine translation. CATM jointly models local sentence context and global document topics to improve lexical selection. It achieves the highest translation performance compared to models using only context or topics. The CATM is the first work to jointly learn both context and topic information for lexical selection in statistical machine translation.[Paper Introduction] Training a Natural Language Generator From Unaligned Data

[Paper Introduction] Training a Natural Language Generator From Unaligned DataNAIST Machine Translation Study Group

The document summarizes a research paper on training a natural language generator from unaligned data. The paper proposes a novel method that integrates the data alignment step into the sentence planning process using deep syntactic trees and rule-based surface realization. This allows the system to learn from incomplete trees and capture long-range syntactic dependencies without requiring a separate alignment step. The method uses an A* search algorithm during sentence planning and is trained on a restaurant domain dataset to generate text from abstract representations, showing improvement over previous work.[Book Reading] 機械翻訳 - Section 5 No.2

This document discusses various techniques for optimizing search space in phrase-based machine translation models, including:
1) Using graph structures and semirings like the tropical semiring to represent translation hypotheses as paths through a weighted graph and find optimal paths.
2) Applying constraints like distortion limits and beam search to prune unpromising partial translations.
3) Using heuristic functions to guide the search and pre-ordering methods like rules and learned models to reorder languages with different word orders.

[Book Reading] 機械翻訳 - Section 7 No.1

The document discusses various methods for optimization in machine translation decoding, including loss minimization, minimum error rate training (MERT), softmax loss, max margin loss, pairwise ranking optimization, and minimum Bayes risk. It covers challenges like non-differentiable error functions and vast search spaces, and how different methods address these challenges through techniques like Powell's method, gradient-based methods, and sentence-level BLEU approximations.

[Book Reading] 機械翻訳 - Section 2 No.2

This document discusses various automatic evaluation metrics for machine translation:
- BLEU evaluates matching n-grams between reference and translated texts but ignores position and favors shorter translations.
- METEOR explicitly matches words accounting for stem, synonym, and paraphrase matches. It aims for high precision and recall.
- RIBES uses rank correlation coefficients between reference and translation word order to evaluate language pairs where word-for-word matching is difficult.
- Statistical testing like bootstrapping is used to determine if differences in evaluation scores between systems are statistically significant.

On using monolingual corpora in neural machine translation

On using monolingual corpora in neural machine translation

[Paper Introduction] Efficient top down btg parsing for machine translation p...

[Paper Introduction] Efficient top down btg parsing for machine translation p...

[Paper Introduction] Translating into Morphologically Rich Languages with Syn...

[Paper Introduction] Translating into Morphologically Rich Languages with Syn...

[Paper Introduction] Supervised Phrase Table Triangulation with Neural Word E...

[Paper Introduction] Supervised Phrase Table Triangulation with Neural Word E...

[Paper Introduction] Evaluating MT Systems with Second Language Proficiency T...

[Paper Introduction] Evaluating MT Systems with Second Language Proficiency T...

[Paper Introduction] Bilingual word representations with monolingual quality ...

[Paper Introduction] Bilingual word representations with monolingual quality ...

[Paper Introduction] A Context-Aware Topic Model for Statistical Machine Tran...

[Paper Introduction] A Context-Aware Topic Model for Statistical Machine Tran...

[Paper Introduction] Training a Natural Language Generator From Unaligned Data

[Paper Introduction] Training a Natural Language Generator From Unaligned Data

[Book Reading] 機械翻訳 - Section 5 No.2

[Book Reading] 機械翻訳 - Section 5 No.2

[Book Reading] 機械翻訳 - Section 7 No.1

[Book Reading] 機械翻訳 - Section 7 No.1

[Book Reading] 機械翻訳 - Section 2 No.2

[Book Reading] 機械翻訳 - Section 2 No.2

学校原版美国波士顿大学毕业证学历学位证书原版一模一样

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Advanced control scheme of doubly fed induction generator for wind turbine us...

This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.

Null Bangalore | Pentesters Approach to AWS IAM

#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)

2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf

2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building

Computational Engineering IITH Presentation

This Presentation will give you a brief idea about what Computational Engineering at IIT Hyderabad has to offer.

CompEx~Manual~1210 (2).pdf COMPEX GAS AND VAPOURS

GAS AND VAPOURS COMPEX 01-04

Generative AI leverages algorithms to create various forms of content

What is Generative AI?

Design and optimization of ion propulsion drone

Electric propulsion technology is widely used in many kinds of vehicles in recent years, and aircrafts are no exception. Technically, UAVs are electrically propelled but tend to produce a significant amount of noise and vibrations. Ion propulsion technology for drones is a potential solution to this problem. Ion propulsion technology is proven to be feasible in the earth’s atmosphere. The study presented in this article shows the design of EHD thrusters and power supply for ion propulsion drones along with performance optimization of high-voltage power supply for endurance in earth’s atmosphere.

Welding Metallurgy Ferrous Materials.pdf

Welding Metallurgy Explained

An Introduction to the Compiler Designss

compiler material

Comparative analysis between traditional aquaponics and reconstructed aquapon...

The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.

一比一原版(CalArts毕业证)加利福尼亚艺术学院毕业证如何办理

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An improved modulation technique suitable for a three level flying capacitor ...

This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.

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Curve Fitting in Numerical Methods Regression

Curve Fitting

哪里办理(csu毕业证书)查尔斯特大学毕业证硕士学历原版一模一样

原版一模一样【微信：741003700 】【(csu毕业证书)查尔斯特大学毕业证硕士学历】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
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AI assisted telemedicine KIOSK for Rural India.pptx

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Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...

Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.

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An improved modulation technique suitable for a three level flying capacitor ...

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原版制作(Humboldt毕业证书)柏林大学毕业证学位证一模一样

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- 1. Language Model MT STUDY MEETING 5/21 HIROYUKI FUDABA
- 2. How can you say whether a sentence is natural or not? 𝑒1 = he is dog 𝑒2 = is big he 𝑒1 = this is a purple dog
- 3. How can you say whether a sentence is natural or not? 𝑒1 = he is dog ↑ correct 𝑒2 = is big he ↑ grammatically wrong 𝑒1 = this is a purple dog ↑ semantically wrong
- 4. Language model probability We want to treat “naturality” statistically We represent this with language model probability 𝑃 𝑒 𝑃 𝑒 = ℎ𝑒 𝑖𝑠 𝑏𝑖𝑔 = 0.7 𝑃 𝑒 = 𝑖𝑠 𝑏𝑖𝑔 ℎ𝑒 = 0.3 𝑃 𝑒 = 𝑡ℎ𝑖𝑠 𝑖𝑠 𝑎 𝑝𝑢𝑟𝑝𝑙𝑒 𝑑𝑜𝑔 = 0.5
- 5. Some ways to estimate 𝑃(𝑒) n-gram model Positional language model factored language model cache language model
- 6. Basis of n-gram we notate a sentence as 𝒆 = 𝑒1 𝐼 , 𝐼 being the length of it 𝑒 = ℎ𝑒 𝑖𝑠 𝑏𝑖𝑔 𝑒1 = ℎ𝑒, 𝑒2 = 𝑖𝑠, 𝑒3 = 𝑏𝑖𝑔, 𝐼 = 3 We can define 𝑃(𝑒) as following 𝑃 𝑒 = ℎ𝑒 𝑖𝑠 𝑏𝑖𝑔 = 𝑃 𝐼 = 3, 𝑒1 = ℎ𝑒, 𝑒2 = 𝑖𝑠, 𝑒3 = 𝑏𝑖𝑔 = 𝑃 𝑒1 = ℎ𝑒, 𝑒2 = 𝑖𝑠, 𝑒3 = 𝑏𝑖𝑔, 𝑒4 = 𝑒𝑜𝑠 = P(e0 = 𝑏𝑜𝑠 , 𝑒1 = ℎ𝑒, 𝑒2 = 𝑖𝑠, 𝑒3 = 𝑏𝑖𝑔, 𝑒4 = 𝑒𝑜𝑠 )
- 7. estimate 𝑃(𝑒) with a simple way assume that natural sentence appear more frequently than the ones that aren’t, simple way to estimate 𝑃(𝑒) is following Bring a big training data 𝐸𝑡𝑟𝑎𝑖𝑛 Count frequencies of each sentences in 𝐸𝑡𝑟𝑎𝑖𝑛 𝑃𝑠 𝑒 = 𝑓𝑟𝑒𝑞 𝑒 𝑠𝑖𝑧𝑒(𝐸𝑡𝑟𝑎𝑖𝑛) = 𝑐𝑡𝑟𝑎𝑖𝑛 𝑒 𝑒 𝑐𝑡𝑟𝑎𝑖𝑛( 𝑒) 𝑐𝑡𝑟𝑎𝑖𝑛 𝑒 = ℎ𝑒 𝑖𝑠 𝑏𝑖𝑔 returns how many sentences exactly matched to “he is big”
- 8. Problem of estimation in simple way when 𝐸𝑡𝑟𝑎𝑖𝑛 does not contain sentences 𝑒1 and 𝑒2, than you can not say which is more natural. 𝑐𝑡𝑟𝑎𝑖𝑛 𝑒1 = 𝑐𝑡𝑟𝑎𝑖𝑛 𝑒2 = 0 𝑃𝑆 𝑒1 = 𝑐 𝑡𝑟𝑎𝑖𝑛 𝑒1 𝑒 𝑐 𝑡𝑟𝑎𝑖𝑛 𝑒 = 0 𝑃𝑆 𝑒2 = 𝑐 𝑡𝑟𝑎𝑖𝑛 𝑒2 𝑒 𝑐 𝑡𝑟𝑎𝑖𝑛 𝑒 = 0 You can not compare if both values are 0 …
- 9. Solution to 𝑃 𝑒 = 0 Rather thinking a sentence as a whole, let’s think that a sentence is a data that is composed of words 𝑃 𝑋, 𝑌 = 𝑃 𝑋 𝑌 𝑃(𝑌) 𝑃 𝑒 = ℎ𝑒 𝑖𝑠 𝑏𝑖𝑔 = 𝑃 𝑒1 = ℎ𝑒 𝑒0 = 𝑏𝑜𝑠 ) ∗ P e2 = is e0 = 𝑏𝑜𝑠 , 𝑒1 = ℎ𝑒) ∗ 𝑃 𝑒3 = 𝑏𝑖𝑔 𝑒0 = 𝑏𝑜𝑠 , 𝑒1 = ℎ𝑒, 𝑒2 = 𝑖𝑠 ∗ 𝑃 𝑒4 = 𝑒𝑜𝑠 𝑒0 = 𝑏𝑜𝑠 , 𝑒1 = ℎ𝑒, 𝑒2 = is, e3 = big)
- 10. Solution to 𝑃 𝑒 = 0 𝑃𝑆 𝑒 = 𝑐𝑡𝑟𝑎𝑖𝑛 𝑒 𝑒 𝑐𝑡𝑟𝑎𝑖𝑛( 𝑒) = 𝑃 𝑒1 𝐼 = 𝑖=1 𝐼+1 𝑃 𝑀𝐿 𝑒𝑖|𝑒0 𝑖−1 𝑃 𝑀𝐿 𝑒𝑖| 𝑒0 𝑖−1 = 𝑐𝑡𝑟𝑎𝑖𝑛 𝑒0 𝑖 𝑐𝑡𝑟𝑎𝑖𝑛(𝑒0 𝑖−1 ) So far 𝑃 𝑒1 𝐼 is completely equal to 𝑃𝑆(𝑒), which means it still don’t work
- 11. Idea of n-gram model Rather considering all words appeared before the word looking at, let’s consider only 𝑛 − 1 words appeared just before the word Instead of considering all words … is big 𝑒𝑜𝑠he𝑏𝑜𝑠
- 12. Idea of n-gram model Rather considering all words appeared before the word looking at, let’s consider only 𝑛 − 1 words appeared just before the word Consider only 𝑛 − 1 words is big 𝑒𝑜𝑠he𝑏𝑜𝑠
- 13. n-gram in precise From the previous expression 𝑃 𝑒1 𝐼 = 𝑖=1 𝐼+1 𝑃 𝑀𝐿 𝑒𝑖|𝑒0 𝑖−1 we can approximate 𝑃(𝑒) as following 𝑃 𝑒1 𝐼 ≈ 𝑖=1 𝐼+1 𝑃 𝑀𝐿 𝑒𝑖|𝑒𝑖−𝑛+1 𝑖−1
- 14. How does this help? 𝑃 𝑒 = ℎ𝑒 𝑖𝑠 𝑏𝑖𝑔 ≈ 𝑃 𝑒𝑖 = ℎ𝑒 | 𝑒𝑖−1 = 𝑏𝑜𝑠 ∗ P ei = is | 𝑒𝑖−1 = he ∗ P ei = big ei−1 = is) ∗ P 𝑒𝑖 = 𝑒𝑜𝑠 | 𝑒𝑖−1 = 𝑏𝑖𝑔 Intuitively, a subset sequence appear more than it’s super set, so 𝑃 𝑒 estimated with n-gram model is less likely to be 0
- 15. Smoothing n-gram model n-gram less likely estimate 𝑃 𝑒 = 0 But it still have a possibility of estimating 0 → Smoothing
- 16. Idea of smoothing Combining probability of n-gram and (n-1)-gram Even if probability of word 𝑤 could not be estimated with n-gram, there is a possibility that probability can be estimated with (n-1)-gram 𝑃3−𝑔𝑟𝑎𝑚 𝑠𝑚𝑎𝑙𝑙 | ℎ𝑒 𝑖𝑠 = 0 P2−gram small is) = 0.03 0 0.05 0.1 0.15 0.2 0.25 P(he|<bos>) P(is|<bos> he) P(big|he is) P(small|he is) P(<eos>|is big) probability probability
- 17. Linear interpolation Easiest, and basic way to express the idea 𝑃 𝑒𝑖|𝑒𝑖−𝑛+1 𝑖−1 = 1 − 𝑎 𝑃 𝑀𝐿 𝑒𝑖|𝑒𝑖−𝑛+1 𝑖−1 + 𝑎𝑃 𝑀𝐿 𝑒𝑖|𝑒𝑖−𝑛+2 𝑖−1 0 ≤ 𝑎 ≤ 1 Adjusting 𝑎 to a good value is the problem So how can we do that?
- 18. Adjusting 𝑎 to a good value Easy way to achieve this is following Bring dataset which is different from training data Select 𝑎 that gives the highest likelihood to the dataset Improve performance by considering each context
- 19. Witten-Bell smoothing How should I choose 𝑎 if n-gram was like following? President was President Ronald elected 5 Reagan 38 the 3 Caza 1 in 3 Venetiaan 1 First 3 … 52 kind, sum 110 3 kind, sum 40
- 20. Witten-Bell smoothing It is likely to have an unknown word after context “President was” 𝑎 should be large, so that (n-1)-gram will be more emphasized 𝑃 𝑒𝑖|𝑒𝑖−𝑛+1 𝑖−1 = 1 − 𝑎 𝑃 𝑀𝐿 𝑒𝑖|𝑒𝑖−𝑛+1 𝑖−1 + 𝑎𝑃 𝑀𝐿 𝑒𝑖|𝑒𝑖−𝑛+2 𝑖−1 President was President Ronald elected 5 Reagan 38 the 3 Caza 1 in 3 Venetiaan 1 First 3 … 52 kind, sum 110 3 kind, sum 40
- 21. Witten-Bell smoothing It is likely to have an unknown word after context “President Ronald” 𝑎 should be small, so that n-gram will be more emphasized 𝑃 𝑒𝑖|𝑒𝑖−𝑛+1 𝑖−1 = 1 − 𝑎 𝑃 𝑀𝐿 𝑒𝑖|𝑒𝑖−𝑛+1 𝑖−1 + 𝑎𝑃 𝑀𝐿 𝑒𝑖|𝑒𝑖−𝑛+2 𝑖−1 President was President Ronald elected 5 Reagan 38 the 3 Caza 1 in 3 Venetiaan 1 First 3 … 52 kind, sum 110 3 kind, sum 40
- 22. Idea of Witten-Bell smoothing If you only had a single coefficient value 𝑎 to adjust, You can not consider context for each word → why not use different 𝒂 to consider each context info for each word?
- 23. Witten-Bell smoothing in precise Simple smoothing 𝑃 𝑒𝑖|𝑒𝑖−𝑛+1 𝑖−1 = 1 − 𝑎 𝑃 𝑀𝐿 𝑒𝑖|𝑒𝑖−𝑛+1 𝑖−1 + 𝑎𝑃 𝑀𝐿 𝑒𝑖|𝑒𝑖−𝑛+2 𝑖−1 Witten-Bell smoothing 𝑃 𝑊𝐵 𝑒𝑖|𝑒𝑖−𝑛+1 𝑖−1 = 1 − 𝑎 𝑒𝑖−𝑛+1 𝑖−1 𝑃 𝑀𝐿 𝑒𝑖|𝑒𝑖−𝑛+1 𝑖−1 + 𝑎 𝑒𝑖−𝑛+1 𝑖−1 𝑃 𝑀𝐿 𝑒𝑖|𝑒𝑖−𝑛+2 𝑖−1 𝑎 𝑒𝑖−𝑛+1 𝑖−1 = 𝑢 𝑒𝑖−𝑛+1 𝑖−1 ,∗ 𝑢 𝑒𝑖−𝑛+1 𝑖−1 ,∗ + 𝑐 𝑒𝑖−𝑛+1 𝑖−1
- 24. Witten-Bell smoothing in precise 𝑎 𝑒𝑖−𝑛+1 𝑖−1 = 𝑢 𝑒𝑖−𝑛+1 𝑖−1 ,∗ 𝑢 𝑒𝑖−𝑛+1 𝑖−1 ,∗ + 𝑐 𝑒𝑖−𝑛+1 𝑖−1 𝑢 𝑒𝑖−𝑛+1 𝑖−1 ,∗ represents how many kind of words continue after context 𝑒𝑖−𝑛+1 𝑖−1 𝑢 𝑃𝑟𝑒𝑠𝑖𝑑𝑒𝑛𝑡 𝑤𝑎𝑠,∗ = 52 𝑢 𝑃𝑟𝑒𝑠𝑖𝑑𝑒𝑛𝑡 𝑅𝑜𝑛𝑎𝑙𝑑,∗ = 3 President was President Ronald elected 5 Reagan 38 the 3 Caza 1 in 3 Venetia an 1 First 3 … 52 kind, sum 110 3 kind, sum 40
- 25. Witten-Bell smoothing in precise 𝑎 𝑒𝑖−𝑛+1 𝑖−1 = 𝑢 𝑒𝑖−𝑛+1 𝑖−1 ,∗ 𝑢 𝑒𝑖−𝑛+1 𝑖−1 ,∗ + 𝑐 𝑒𝑖−𝑛+1 𝑖−1 𝑎 𝑃𝑟𝑒𝑠𝑖𝑑𝑒𝑛𝑡 𝑤𝑎𝑠 = 52 110+52 = 0.32 𝑎 𝑃𝑟𝑒𝑠𝑖𝑑𝑒𝑛𝑡 𝑅𝑜𝑛𝑎𝑙𝑑 = 3 40+3 = 0.07 President was President Ronald elected 5 Reagan 38 the 3 Caza 1 in 3 Venetia an 1 First 3 … 52 kind, sum 110 3 kind, sum 40
- 26. Absolute discounting Yet another smoothing Unlike Witten-Bell smoothing which uses 𝑃 𝑀𝐿, it subtracts constant value 𝑑 from frequency of each word in order to estimate probability 𝑃𝑑 𝑒𝑖 | 𝑒0 𝑖−1 = max 𝑐𝑡𝑟𝑎𝑖𝑛 𝑒0 𝑖 − 𝑑, 0 𝑐𝑡𝑟𝑎𝑖𝑛 𝑒0 𝑖−1
- 27. Abstruct discounting So why do you subtract? We want to treat low-frequent word as unknown word, because low-frequent one can not really be trusted. By doing this, (n-1)-gram gets more emphasized
- 28. Absolute discounting 𝑃𝑑 𝑒𝑖 | 𝑒𝑖−𝑛+1 𝑖−1 = max 𝑐𝑡𝑟𝑎𝑖𝑛 𝑒𝑖−𝑛+1 𝑖 − 𝑑, 0 𝑐𝑡𝑟𝑎𝑖𝑛 𝑒𝑖−𝑛+1 𝑖−1 𝑃𝑑 𝑒𝑖 = 𝑟𝑒𝑎𝑔𝑎𝑛|𝑒𝑖−2 𝑖−1 = 𝑝𝑟𝑒𝑠𝑖𝑑𝑒𝑛𝑡 𝑟𝑜𝑛𝑎𝑙𝑑 = 38 − 0.5 40 = 0.9375 𝑃𝑑 𝑒𝑖 = 𝑐𝑎𝑧𝑎|𝑒𝑖−2 𝑖−1 = 𝑝𝑟𝑒𝑠𝑖𝑑𝑒𝑛𝑡 𝑟𝑜𝑛𝑎𝑙𝑑 = 1 − 0.5 40 = 0.0125 𝑃𝑑 𝑒𝑖 = 𝑣𝑒𝑛𝑒𝑡𝑖𝑎𝑎𝑛|𝑒𝑖−2 𝑖−1 = 𝑝𝑟𝑒𝑠𝑖𝑑𝑒𝑛𝑡 𝑟𝑜𝑛𝑎𝑙𝑑 = 1 − 0.5 40 = 0.0125 President was President Ronald elected 5 Reagan 38 the 3 Caza 1 in 3 Venetia an 1 First 3 … 52 kind, sum 110 3 kind, sum 40
- 29. Absolute discounting 𝑃𝑑 𝑒𝑖 = 𝑟𝑒𝑎𝑔𝑎𝑛|𝑒𝑖−2 𝑖−1 = 𝑝𝑟𝑒𝑠𝑖𝑑𝑒𝑛𝑡 𝑟𝑜𝑛𝑎𝑙𝑑 = 0.9375 𝑃𝑑 𝑒𝑖 = 𝑐𝑎𝑧𝑎|𝑒𝑖−2 𝑖−1 = 𝑝𝑟𝑒𝑠𝑖𝑑𝑒𝑛𝑡 𝑟𝑜𝑛𝑎𝑙𝑑 = 0.0125 𝑃𝑑 𝑒𝑖 = 𝑣𝑒𝑛𝑒𝑡𝑖𝑎𝑎𝑛|𝑒𝑖−2 𝑖−1 = 𝑝𝑟𝑒𝑠𝑖𝑑𝑒𝑛𝑡 𝑟𝑜𝑛𝑎𝑙𝑑 = 0.0125 𝑎 𝑒𝑖−𝑛+1 𝑖−1 = 1 − 0.9375 + 0.0125 + 0.0125 = 0.0375 Efficient way of solving this is following 𝑎 𝑒𝑖−𝑛+1 𝑖−1 = 𝑢 𝑒𝑖−𝑛+1 𝑖−1 ,∗ × 𝑑 𝑐 𝑒𝑖−𝑛+1 𝑖−1
- 30. Absolute discounting Now that we do not use maximum likelihood, n-gram probability will be estimated as following 𝑃 𝑒𝑖| 𝑒𝑖−𝑛+1 𝑖−1 = 𝑃𝑑 𝑒𝑖|𝑒𝑖−𝑛+1 𝑖−1 + 𝑎 𝑒𝑖−𝑛+1 𝑖−1 𝑃 𝑒𝑖|𝑒𝑖−𝑛+2 𝑖−1 Quite similar, but differs in that absolute discounting use 𝑃𝑑 𝑃 𝑒𝑖|𝑒𝑖−𝑛+1 𝑖−1 = 1 − 𝑎 𝑃 𝑀𝐿 𝑒𝑖|𝑒𝑖−𝑛+1 𝑖−1 + 𝑎𝑃 𝑀𝐿 𝑒𝑖|𝑒𝑖−𝑛+2 𝑖−1
- 31. Kneser-Ney smoothing achieve excellent performance Similar to absolute discounting Have an interest in a word that only appears in specific context
- 32. Kneser-Ney smoothing Lower order model is needed only when count in higher order model is small Suppose “San Francisco” is common, but “Francisco” appears only after “San” Both “San” and “Francisco” get a high unigram probability But we want to give “Francisco” a low unigram probability!!
- 33. Kneser-Ney smoothing Kneser-Ney is defined as following 𝑃𝑘𝑛 𝑒𝑖|𝑒𝑖−𝑛+1 𝑖−1 = max 𝑢 ∗, 𝑒𝑖−𝑛+2 𝑖−1 − d, 0 𝑢 𝑒𝑖−𝑛+1 𝑖−1
- 34. Unknown words Even though smoothing can reduce probability of having 𝑃 𝑒 = 0, possibility of getting 0 still rely We may give a possibility to unknown word as following 𝑃𝑢𝑛𝑘 𝑒𝑖 = 1 𝑉