Noah  Rowe

Noah Rowe

1593820620

Algorithm of invention is the manager’s best friend

My first education is related to mechanical engineering, and despite my involvement in data science for more than 16 years, many ideas from engineering empower me and drive to make stronger decisions when I work as a data science manager.

I remember my fourth year in university, when our professor ( doctor of science in his 36 ) has been walking slowly over a huge old-fashioned study room with large benches straight from the Harry Potter movie. He was a very concise and good lecturer, that’s why his introduction to the Algorithm of Invention was the brightest course I ever attended and now I’m using it in my everyday data science work. Henceforth I would like to tell you some more about the automation of a data science and how to use it to plan the evolution of ML projects.

As soon as the article is going to be quite long, let me present a short plan:

  • First, I will introduce some concepts from general morphological analysis and Zwicky box as most notable concept of this theory
  • Then I’m going to present some examples of how Zwicky box can be used in data science, illustrated by winning solutions of well-known Kaggle competitions
  • Central part of an article is dedicated to the idea of _ML legacy — _the fact that every model ( especially most popular ) has an enormous layer of rarely revisited concepts, by improving which it’s possible to advance the performance of your models ‘for free’, i.e. without much feature engineering, trial and error, and tuning
  • Finally, I will provide specific examples of utilizing legacy as a Zwicky axis and then conclude with thought-provoking things.

Basics of Zwicky box

General Morphological Analysis (GMA) was developed by Fritz Zwicky — the Swiss astrophysicist and aerospace scientist based at the California Institute of Technology (Caltech) — as a method for structuring and investigating the total set of relationships contained in multi-dimensional, non-quantifiable, innovative problem complexes.

Zwicky applied this method to such diverse fields as the classification of astrophysical objects, the development of jet and rocket propulsion systems, and the legal aspects of space travel and colonization. He founded the Society for Morphological Research and enthusiastically advanced the “morphological approach” for some 30 years — between the 1940s until his death in 1974.

Zwicky box ( shown below on a picture ) is a key concept of general morphology:

Taken from http://www.swemorph.com/pdf/gma.pdf

The approach begins by identifying and defining the parameters (or dimensions) of the problem complex to be investigated, and assigning each parameter a range of relevant “values” or conditions. A morphological box — also fittingly known as a “Zwicky box” — is constructed by setting the parameters against each other in an n-dimensional matrix (see Figure 1a). Each cell of the n-dimensional box contains one particular “value” or condition from each of the parameters, and thus marks out a particular state or configuration of the problem complex.

For example, imagine a simple problem complex, which we define as consisting of three dimensions — let us say “colour”, “texture” and “size”. In order to conform to Figure 1a, let us further define the first two dimensions as consisting of 5 discrete “values” or conditions each (e.g. colour = red, green, blue, yellow, brown) and the third consisting of 3 values (size = large, medium, small). We then have 5x5x3 (= 75) cells in the Zwicky box, each containing 3 conditions — i.e. one from each dimension (e.g. red, rough, large). The entire 3-dimensional matrix is a typological field containing all of the (formally) possible relationships involved.

This concrete example utilizes just three dimensions, but in real problems one is free to create as many dimensions and axes as necessary. Have a look at the concrete example from engineering:

Taken from http://www.swemorph.com/ma.html

How to read this table? Grey boxes denote axes, blue boxes indicate one possible choice of requirements for the bomb shelter, in this case, the shelter is supposed to be designed for small cities, with humanitarian aims, large, not crammed, with new construction, no specific requirements for maintenance and priorities for those in need. By selecting different cells we can instantly get some other shelter, very convenient.

Zwicky box in data science

So far so good, but… will it work in data science? For sure yes!

Let’s consider two ML competitions as an illustration. The first one had the objective to identify which E-commerce buyers can be converted to regular loyal buyers to then target them to reduce promotion cost and increase the return on investment (ROI). At International Joint Conferences on Artificial Intelligence (IJCAI) 2015, Alibaba hosted an international competition for recurring buyers prediction based on the sales data of the “Double 11” shopping event in 2014 at Tmall.com. Let’s have a look at the central figure from the paper, outlining the winning solution:

How winners of competition managed to generate lots of features

In their paper authors call Zwicky axes profiles and even apply operators on axes to generate features. Paper proposes _interaction, averaging, diversity, recency, _and many other axes, so to make a clear illustration let‘s consider how Zwicky box can be used to generate features.

Say our axes are _user _and merchant and operator is overall count ( i.e. we count things using whole past data without constraints like past week/day/etc ). Then by applying count operator on user axis we can get these features:


It’s very straightforward to create similar features for merchants ( i.e. merchant and counter are our axes ) :

To implement the _recency _axis we can constrain the count operator by some timeframe ( say, 1 recent week ). Then by combining _user, merchant, _and _recency _axes we can get respective features easily:

By continuing in this way one can get _the idea of _thousands of features in just a few minutes! Obviously not all of them will be equally useful, so feature selection is an important part of ML modelling pipeline, but for beginner data scientists just _generating ideas of features _is something tedious and difficult to manage. Henceforth, it’s very rewarding to automate this process.

And indeed, if these operators above are binary and associative, then by introducing trivial axis ( which simply leaves features as is without generation ) we can even form a Zwicky _monoid _on axes and thus generate features automatically ( see this brilliant post for more details about monoids ). By doing so, authors of a winning solution have created more than 1000 features and overall tested many more.

Another brilliant example of this sort for me is Facebook Missing Link Prediction Challenge. The winning solution used Zwicky box implicitly as well in order to generate features. Here are top features, can you spot Zwicky axes there?

To read a complete story, please read Edwin Chen’s 10 place solution

But is it really just about feature engineering?

For sure no. General morphological analysis is a comprehensive method and “is simply an ordered way of looking at things” ( as Zwicky used to say ), henceforth it can be applied to automate the whole process of creativity and for this reason, it is widespread in engineering, arts and many other areas where creativity is a cornerstone. But let’s define some axes to answer our question constructively. How about legacy as an axis?

Legacy and reusability are the cornerstones of both engineering and data science. Almost all common data scientists call model.fit() without any second thought and with few glitches this behavior won’t deliver any bad scores. But sometimes you simply have to stop and start out of the box thinking, because the pay off can be generous.

A very common managerial decision to pick low hanging fruits is to design the simplest possible model ( linear, kNN or the like ) or even avoid any modeling and try some hand-tuned knobs. But take a careful look: what if you can take way more powerful model currently in production and overhaul it with just one minor modification?

Take a look at this sequence of concepts ( “graph” “random walk”-> “list of visited nodes” -> “word2vec” -> “embeddings” ) and let’s spot small but powerful improvements. Sampling is the first that caught my eye:

#deep-learning #data-science #algorithms

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Algorithm of invention is the manager’s best friend
Stratus seo

Stratus seo

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Stratus: One of the best social media posting tools for efficient social media management

Efficient social media management could mean you getting the desired online recognition and leads for your business (if that was your intend to stay active on social media). Unfortunately, the common practice of social media management requires you to switch between multiple accounts of yours. This requires significant time and effort on your part. Stratus addresses this problem by bringing all of the social media channels on a single platform. You can access and manage your social media accounts in a single place while saving your time and effort. The user-friendly interface and advanced features integrated into the Stratus platform make it one of the best social media posting tools. To learn more or to sign up on Stratus, visit https://stratus.co/

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Origin Scale

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Originscale Order Management System

Originscale order management software helps to manage all your orders across channels in a single place. Originscale collects orders across multiple channels in real-time - online, offline, D2C, B2B, and more. View all your orders in one single window and process them with a simple click.

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Jessica Smith

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Real Estate Property Management Software Development in USA | SISGAIN

We at SISGAIN, best real estate property management software solutions are making it easy to grow, automate payments and transactions, streamline leasing, optimize the operating budget, and improve services and rapport with tenants. SISGAIN is an all-in-one online property management automation solution that gives you all the security and power of the cloud, while saving your budget. SISGAIN’s commercial property management software platform provides the right kind of service for Landlords, Property Management Firms and Mid sized property rental firms which are scaling up to service more tenants. For more information call us at +18444455767 or email us at hello@sisgain.com

#real estate property management software #commercial property management software #best rental management software #real estate project management software #best property management app #real estate crm software

Noah  Rowe

Noah Rowe

1593820620

Algorithm of invention is the manager’s best friend

My first education is related to mechanical engineering, and despite my involvement in data science for more than 16 years, many ideas from engineering empower me and drive to make stronger decisions when I work as a data science manager.

I remember my fourth year in university, when our professor ( doctor of science in his 36 ) has been walking slowly over a huge old-fashioned study room with large benches straight from the Harry Potter movie. He was a very concise and good lecturer, that’s why his introduction to the Algorithm of Invention was the brightest course I ever attended and now I’m using it in my everyday data science work. Henceforth I would like to tell you some more about the automation of a data science and how to use it to plan the evolution of ML projects.

As soon as the article is going to be quite long, let me present a short plan:

  • First, I will introduce some concepts from general morphological analysis and Zwicky box as most notable concept of this theory
  • Then I’m going to present some examples of how Zwicky box can be used in data science, illustrated by winning solutions of well-known Kaggle competitions
  • Central part of an article is dedicated to the idea of _ML legacy — _the fact that every model ( especially most popular ) has an enormous layer of rarely revisited concepts, by improving which it’s possible to advance the performance of your models ‘for free’, i.e. without much feature engineering, trial and error, and tuning
  • Finally, I will provide specific examples of utilizing legacy as a Zwicky axis and then conclude with thought-provoking things.

Basics of Zwicky box

General Morphological Analysis (GMA) was developed by Fritz Zwicky — the Swiss astrophysicist and aerospace scientist based at the California Institute of Technology (Caltech) — as a method for structuring and investigating the total set of relationships contained in multi-dimensional, non-quantifiable, innovative problem complexes.

Zwicky applied this method to such diverse fields as the classification of astrophysical objects, the development of jet and rocket propulsion systems, and the legal aspects of space travel and colonization. He founded the Society for Morphological Research and enthusiastically advanced the “morphological approach” for some 30 years — between the 1940s until his death in 1974.

Zwicky box ( shown below on a picture ) is a key concept of general morphology:

Taken from http://www.swemorph.com/pdf/gma.pdf

The approach begins by identifying and defining the parameters (or dimensions) of the problem complex to be investigated, and assigning each parameter a range of relevant “values” or conditions. A morphological box — also fittingly known as a “Zwicky box” — is constructed by setting the parameters against each other in an n-dimensional matrix (see Figure 1a). Each cell of the n-dimensional box contains one particular “value” or condition from each of the parameters, and thus marks out a particular state or configuration of the problem complex.

For example, imagine a simple problem complex, which we define as consisting of three dimensions — let us say “colour”, “texture” and “size”. In order to conform to Figure 1a, let us further define the first two dimensions as consisting of 5 discrete “values” or conditions each (e.g. colour = red, green, blue, yellow, brown) and the third consisting of 3 values (size = large, medium, small). We then have 5x5x3 (= 75) cells in the Zwicky box, each containing 3 conditions — i.e. one from each dimension (e.g. red, rough, large). The entire 3-dimensional matrix is a typological field containing all of the (formally) possible relationships involved.

This concrete example utilizes just three dimensions, but in real problems one is free to create as many dimensions and axes as necessary. Have a look at the concrete example from engineering:

Taken from http://www.swemorph.com/ma.html

How to read this table? Grey boxes denote axes, blue boxes indicate one possible choice of requirements for the bomb shelter, in this case, the shelter is supposed to be designed for small cities, with humanitarian aims, large, not crammed, with new construction, no specific requirements for maintenance and priorities for those in need. By selecting different cells we can instantly get some other shelter, very convenient.

Zwicky box in data science

So far so good, but… will it work in data science? For sure yes!

Let’s consider two ML competitions as an illustration. The first one had the objective to identify which E-commerce buyers can be converted to regular loyal buyers to then target them to reduce promotion cost and increase the return on investment (ROI). At International Joint Conferences on Artificial Intelligence (IJCAI) 2015, Alibaba hosted an international competition for recurring buyers prediction based on the sales data of the “Double 11” shopping event in 2014 at Tmall.com. Let’s have a look at the central figure from the paper, outlining the winning solution:

How winners of competition managed to generate lots of features

In their paper authors call Zwicky axes profiles and even apply operators on axes to generate features. Paper proposes _interaction, averaging, diversity, recency, _and many other axes, so to make a clear illustration let‘s consider how Zwicky box can be used to generate features.

Say our axes are _user _and merchant and operator is overall count ( i.e. we count things using whole past data without constraints like past week/day/etc ). Then by applying count operator on user axis we can get these features:


It’s very straightforward to create similar features for merchants ( i.e. merchant and counter are our axes ) :

To implement the _recency _axis we can constrain the count operator by some timeframe ( say, 1 recent week ). Then by combining _user, merchant, _and _recency _axes we can get respective features easily:

By continuing in this way one can get _the idea of _thousands of features in just a few minutes! Obviously not all of them will be equally useful, so feature selection is an important part of ML modelling pipeline, but for beginner data scientists just _generating ideas of features _is something tedious and difficult to manage. Henceforth, it’s very rewarding to automate this process.

And indeed, if these operators above are binary and associative, then by introducing trivial axis ( which simply leaves features as is without generation ) we can even form a Zwicky _monoid _on axes and thus generate features automatically ( see this brilliant post for more details about monoids ). By doing so, authors of a winning solution have created more than 1000 features and overall tested many more.

Another brilliant example of this sort for me is Facebook Missing Link Prediction Challenge. The winning solution used Zwicky box implicitly as well in order to generate features. Here are top features, can you spot Zwicky axes there?

To read a complete story, please read Edwin Chen’s 10 place solution

But is it really just about feature engineering?

For sure no. General morphological analysis is a comprehensive method and “is simply an ordered way of looking at things” ( as Zwicky used to say ), henceforth it can be applied to automate the whole process of creativity and for this reason, it is widespread in engineering, arts and many other areas where creativity is a cornerstone. But let’s define some axes to answer our question constructively. How about legacy as an axis?

Legacy and reusability are the cornerstones of both engineering and data science. Almost all common data scientists call model.fit() without any second thought and with few glitches this behavior won’t deliver any bad scores. But sometimes you simply have to stop and start out of the box thinking, because the pay off can be generous.

A very common managerial decision to pick low hanging fruits is to design the simplest possible model ( linear, kNN or the like ) or even avoid any modeling and try some hand-tuned knobs. But take a careful look: what if you can take way more powerful model currently in production and overhaul it with just one minor modification?

Take a look at this sequence of concepts ( “graph” “random walk”-> “list of visited nodes” -> “word2vec” -> “embeddings” ) and let’s spot small but powerful improvements. Sampling is the first that caught my eye:

#deep-learning #data-science #algorithms

BSE tec

BSE tec

1634193233

FAQs On a Udemy clone app That You Should Know Before Buying!

learning system based on formalised teaching but with the help of electronic resources is known as E-learning.E-learning, also referred to as online learning or electronic learning, is the acquisition of knowledge which takes place through electronic technologies .

Udemy, which was founded more than a decade ago, has established itself as a global leader in the online teaching industry. People in our digital era rely on technology and devices for nearly everything, including education. When it comes to online learning, or eLearning, which is regarded as a lifesaver– capable of sustaining the educational environment even during the Covid-19 pandemic– it has evolved into a platform bursting with potential, not only for students but also for tutors, educators, and instructors. Furthermore, when compared to traditional educational techniques, the flexibility and customisation given by online tutoring software are unrivalled. As a result, it is gradually becoming an important component of the learning process, which is why eLearning is here to stay– even after the epidemic has passed, it is a huge increase in the number of eLearning applications and websites, and most businesses are choosing for Udemy Clone App instead of starting from scratch.

learn BigBlueButton Technology – Make Efficient Interaction with LMS

How can I get started with your own online tutoring company?

To use the Udemy Clone App script to establish an online tutoring marketplace similar to Udemy, bear the following factors in mind:

  • How to define your target audience?– Before you start developing your Learning Management System, you should think about who you're going to serve, what their background is, what kind of job they want to do, and what their career goals are. These are all things to consider before you begin the development process. As a result, you must be clear about who you are developing your app for.
  • Why have a clear set of goals and objectives in mind?– If you're creating an app for eLearning, you should have a solid strategy and set of goals in mind. You can't simply take a leap of faith and draw conclusions. You must have both long and short term objectives, and you must put them into action.
  • Why create a simple and speedy registration process?– No one wants to spend more than a minute or two on a signup screen, especially if the app is intended for educational reasons. As a result, it's critical that the user registration, login, and signup procedure be simple and quick. It should provide one-tap or social login options so that the AI can read the social account and fill in the data on its own.
  • Why have a visually appealing homepage– "The first impression is the final impression" and "The last impression is the enduring impression" are two prominent expressions. When you initially open the app, the first thing that appears on your screen is the homepage, and if it isn't appealing enough, you will most likely not want to use it. As a result, the user interface must be simple to use and appealing to the eye.
  • Why do we alter and revise– Once you've begun creating the app, you can customise and revise it to meet the needs of your users, thanks to the Udemy Clone Script's high level of customization and a large range of templates and motifs to select from.


If you want to develop a platform with high-quality eLearning features that cater to the needs of a large mass of audience, then opt for ExpertPlus LMS, and contact BSEtec now! Hurry!

#Best Learning Management Software #Udemy Clone Script  #Best LMS App #Best LMS Platform #Best Open Source Web Conferencing System #Learning Management Portal #Learning Management Software