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Fractionally Differentiated Features
Introduction
Fractional differentiation (or Fractional derivative or Fractional calculus) is a great idea once you understood all the equations. But the first time when I was reading Chapter 5 of Advances in Financial Machine Learning by Marcos Prado. All the mathematical symbols and equations just push me away from it.
I was like, what is this?
Believe me, if you feel lost, you are not alone. The reactions of most people just like what Homer Simpson said: “If something’s hard to do, then it’s not worth doing.” Luckily, I was one of the most people until now.
Let’s face it and it wasn’t as hard as it looks like. We can break it down and figure it out step by step with the first principle.
Ok, let’s break down the whole chapter and talk it through… together, emmm.…I mean together…
#machine-learning #python #finance #fractional-calculus #marco-prato
What is GEEK
Buddha Community
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Getting Started With Feature Flags
Introduction
As any developer can tell you, deploying any code carries technical risk. Software might crash or bugs might emerge. Deploying features carries additional user-related risk. Users might hate the new features or run into account management issues. With traditional deployments, all of this risk is absorbed at once.
Feature flags give developers the ability to separate these risks, dealing with one at a time. They can put the new code into production, see how that goes, and then turn on the features later once it’s clear the code is working as expected.
What is a Feature Flag?
Simply put, a feature flag is a way to change a piece of software’s functionality without changing and re-deploying its code. Feature flags involve creating a powerful “if statement” surrounding some chunk of functionality in software (pockets of source code).
The History of Feature Flags
Leading Web 2.0 companies with platforms and services that must maintain performance among high traffic levels led the way in regard to developing and popularizing new deployment techniques. Facebook, in particular, is known as a pioneer of feature flags and for releasing massive amounts of code at scale. While building its massive social network more than a decade ago, the company realized that its uptime and scale requirements could not be met with traditional site maintenance approaches. (A message saying the site was down while they deployed version 3.0 was not going to cut it).
Instead, Facebook just quietly rolled out a never-ending stream of updates without fanfare. Day to day, the site changed in subtle ways, adding and refining functionality. At the time, this was a mean feat of engineering. Other tech titans such as Uber and Netflix developed similar deployment capabilities as well.
The feature flag was philosophically fundamental to this development and set the standard for modern deployment maturity used by leading organizations everywhere today. Recently, feature flags have been used in tandem with continuous delivery (CD) tools to help forward-looking organizations bring features, rather than releases, to market more quickly.
#devops #continuous integration #ci/cd #continous delivery #feature flags #flags #feature branching #feature delivery
1598626680
Fractionally Differentiated Features
Introduction
Fractional differentiation (or Fractional derivative or Fractional calculus) is a great idea once you understood all the equations. But the first time when I was reading Chapter 5 of Advances in Financial Machine Learning by Marcos Prado. All the mathematical symbols and equations just push me away from it.
I was like, what is this?
Believe me, if you feel lost, you are not alone. The reactions of most people just like what Homer Simpson said: “If something’s hard to do, then it’s not worth doing.” Luckily, I was one of the most people until now.
Let’s face it and it wasn’t as hard as it looks like. We can break it down and figure it out step by step with the first principle.
Ok, let’s break down the whole chapter and talk it through… together, emmm.…I mean together…
#machine-learning #python #finance #fractional-calculus #marco-prato
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Tinder Features: A complete and in-detail list
There are a wide number of dating apps around the world, but only Tinder became successful. Do you know the reason for its success? The primary reason why tinder is able to attract users around the globe is because of its simple yet attractive features.
Due to distinct features along with the highly-interactive UI UX, millions of users are getting attracted to this dating app.
Do you want to know the features present in the most popular dating app in the world, then we have got you covered. Here, we have made an exclusive list of all the Tinder Features.
#Tinder Features #Tinder gold features #Tinder App Features
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Feature Engineering & Feature Selection
Warning: There is no magical formula or Holy Grail here, though a new world might open the door for you.
📈Python for finance series
- Identifying Outliers
- Identifying Outliers — Part Two
- Identifying Outliers — Part Three
- Stylized Facts
- Feature Engineering & Feature Selection
- Data Transformation
Following up the previous posts in these series, this time we are going to explore a real Technical Analysis (TA) in the financial market. For a very long time, I have been fascinated by the inner logic of TA called Volume Spread Analysis (VSA). I have found no articles on applying modern Machine learning on this time proving long-lasting technique. Here I am trying to throw out a minnow to catch a whale. If I could make some noise in this field, it was worth the time I spent on this article.
Especially, after I read David H. Weis’s Trades About to Happen, in his book he described:
“Instead of analyzing an array of indicators or algorithms, you should be able to listen to what any market says about itself.”¹
To closely listen to the market, as also well said from this quote below, just as it may not be possible to predict the future, it is also hard to neglect things about to happen. The key is to capture what is about to happen and follow the flow.
But how to perceive things about to happen, a statement made long ago by Richard Wyckoff gives some clues:
“Successful tape reading [chart reading] is a study of Force. It requires ability to judge which side has the greatest pulling power and one must have the courage to go with that side. There are critical points which occur in each swing just as in the life of a business or of an individual. At these junctures it seems as though a feather’s weight on either side would determine the immediate trend. Any one who can spot these points has much to win and little to lose.”²
#feature-engineering #feature-selection #trading #python #machine-learning
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Feature Engineering: What is Feature Engineering?
According to a survey in Forbes, data scientists spend 80% of their time on data preparation. This shows the importance of feature engineering in data science. Here are some valuable quotes about Feature Engineering and its importance:
Coming up with features is difficult, time-consuming, requires expert knowledge. ‘Applied machine learning’ is basically feature engineering — Prof. Andrew Ng.
The features you use influence more than everything else the result. No algorithm alone, to my knowledge, can supplement the information gain given by correct feature engineering — Luca Massaron
What is Feature Engineering?
Feature engineering is the process of transforming raw data into features that better represent the underlying problem to the predictive models, resulting in an improved model accuracy on unseen data.
Basically, all machine learning algorithms use some input data to create outputs. This input data comprises features, which are usually in the form of structured columns. Algorithms require features with some specific characteristic to work properly
Having and engineering good features will allow us to most accurately represent the underlying structure of the data and therefore create the best model. Features can be engineered by decomposing or splitting features, from external data sources, or aggregating or combining features to create new features.
#data-science #feature-engineering #feature-selection #data analysis