uncleflo
Registered since September 28th, 2017
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Introduction to bookkeeping and accounting - OpenLearn - Open University - B190_1
Saved by uncleflo on January 18th, 2022.
Learn about the essential numerical skills required for accounting and bookkeeping. This free course, Introduction to bookkeeping and accounting, explains the fundamental rules of double-entry bookkeeping and how they are used to produce the balance sheet and the profit and loss account. You can start this course right now without signing-up. Click on any of the course content sections below to start at any point in this course. If you want to be able to track your progress, earn a free Statement of Participation, and access all course quizzes and activities, sign-up. Review and track your learning through your OpenLearn Profile. On completion of a course you will earn a Statement of Participation.
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python - How do I fit a sine curve to my data with pylab and numpy? - Stack Overflow
Saved by uncleflo on May 15th, 2021.
I am trying to show that economies follow a relatively sinusoidal growth pattern. I am building a python simulation to show that even when we let some degree of randomness take hold, we can still produce something relatively sinusoidal. I am happy with the data I'm producing, but now I'd like to find some way to get a sine graph that pretty closely matches the data. I know you can do polynomial fit, but can you do sine fit? You can use the least-square optimization function in scipy to fit any arbitrary function to another. In case of fitting a sin function, the 3 parameters to fit are the offset ('a'), amplitude ('b') and the phase ('c').
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How TF-IDF algorithm determines keyword importance - arbitrue Blog
Saved by uncleflo on December 23rd, 2018.
There are many tools in the developer’s toolbox when it comes to automatic data extraction. A good example is TF-IDF algorithm (Term Frequency – Inverse Document Frequency) which helps the system understand the importance of keywords extracted using OCR. Here’s how TF-IDF can be used for invoice and receipt recognition. In this article we focus on other techniques in order to make this text file “understandable” to a computer. For this purpose, we must delve into the world of NLP or Natural Language Processing. We will focus mainly on how we can transform our file of raw text into a format that will easily be understandable by our algorithm. In a nutshell, TF-IDF is a technique for understanding how important a word is in a document which is often used as a weighting factor for numerous use cases. TF-IDF takes under consideration how frequent a word appears in a single document in relation to how frequent that word is in general. Search engines can use TF-IDF to determine which results are the most relevant for a search query.
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Term Frequency and Inverse Document Frequency (tf-idf) Using Tidy Data Principles
Saved by uncleflo on December 23rd, 2018.
A central question in text mining and natural language processing is how to quantify what a document is about. Can we do this by looking at the words that make up the document? One measure of how important a word may be is its term frequency (tf), how frequently a word occurs in a document. There are words in a document, however, that occur many times but may not be important; in English, these are probably words like “the”, “is”, “of”, and so forth. We might take the approach of adding words like these to a list of stop words and removing them before analysis, but it is possible that some of these words might be more important in some documents than others. A list of stop words is not a sophisticated approach to adjusting term frequency for commonly used words. Another approach is to look at a term’s inverse document frequency (idf), which decreases the weight for commonly used words and increases the weight for words that are not used very much in a collection of documents. This can be combined with term frequency to calculate a term’s tf-idf, the frequency of a term adjusted for how rarely it is used. It is intended to measure how important a word is to a document in a collection (or corpus) of documents. It is a rule-of-thumb or heuristic quantity; while it has proved useful in text mining, search engines, etc., its theoretical foundations are considered less than firm by information theory experts.
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Different Types of Chairs For Your Living Room!
Saved by uncleflo on November 29th, 2018.
Merriam Webster dictionary defines ‘chairs’ as a seat typically having the legs and a back for one person. It’s interesting that following this simple definition we have around us so many variations of this basic type of furniture. There are different types of chairs that you must know before you plan for your home furniture. For every activity that we do while sitting our comfortable postures needed are different and so the types of chairs, we need for the same should also be differently designed! For instance, a dining chair cannot be used for relaxing and vice versa. It is for the same reason that, more than colour and material it is the usage which makes one chair different from another.
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Combustion Elearning Cerfacs
Saved by uncleflo on July 16th, 2016.
Theoretical And Numerical Combustion, Recorded master courses, Additional materials for master courses
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Isotropic Linear Elastic Stress Concentration, Massachusetts Institute of Technology
Saved by uncleflo on September 2nd, 2014.
The primary objectives of this lab are to introduce the concept of stress and strain concentration factors in notched structural configurations. The notion of stress concentration is experimentally explored qualitatively, using photoelasticity, and quantitatively, using experimental, analytical, and numerical methods. 2.002 Mechanics and Materials II, Spring 2004
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2D Convolution
Saved by uncleflo on May 19th, 2014.
Convolution is the most important and fundamental concept in signal processing and analysis. By using convolution, we can construct the output of system for any arbitrary input signal, if we know the impulse response of system. How is it possible that knowing only impulse response of system can determine the output for any given input signal? We will find out the meaning of convolution.
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Cost of Living in Hong Kong. Prices in Hong Kong.
Saved by uncleflo on March 17th, 2013.
Cost of Living in Hong Kong. Numbeo is the world’s largest database of user contributed data about cities and countries worldwide. Numbeo provides current and timely information on world living conditions including cost of living, housing indicators, health care, traffic, crime and pollution. Numbeo is a collection of Web pages containing numerical and other itemizable data about cities and countries, designed to enable anyone to contribute or modify content. Numbeo uses the wisdom of the crowd to obtain the most reliable information possible. Numbeo then provides you with a statistical analysis of the data collected. In addition, Numbeo provides a variety of systematic research opportunities for its readers with its compilation of worldwide information.
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