But what p-value means in #MachineLearning - A thread

It tells you how likely it is that your data could have occurred under the null hypothesis

1/n

#DataScience #DeepLearning #ComputerVision #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Data #Math #Stat

It tells you how likely it is that your data could have occurred under the null hypothesis

1/n

#DataScience #DeepLearning #ComputerVision #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Data #Math #Stat

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What Is a Null Hypothesis?

A null hypothesis is a type of statistical hypothesis that proposes that no statistical significance exists in a set of given observations.

#DataScience #MachineLearning #100DaysOfMLCode #Python #stat #Statistics #Data #AI #Math #deeplearning

What Is a Null Hypothesis?

A null hypothesis is a type of statistical hypothesis that proposes that no statistical significance exists in a set of given observations.

#DataScience #MachineLearning #100DaysOfMLCode #Python #stat #Statistics #Data #AI #Math #deeplearning

3/n

A P-value is the probability of obtaining an effect at least as extreme as the one in your sample data, assuming the truth of the null hypothesis

#DataScience #MachineLearning #100DaysOfMLCode #Python #DataScientist #Statistics #Data #DataAnalytics #AI #Math

A P-value is the probability of obtaining an effect at least as extreme as the one in your sample data, assuming the truth of the null hypothesis

#DataScience #MachineLearning #100DaysOfMLCode #Python #DataScientist #Statistics #Data #DataAnalytics #AI #Math

A thread on AUC Score (Area under the ROC Curve) Interpretation in #DataScience #MachineLearning

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#DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Math #Data #DataAnalytics #pythoncode #AI #ArtificialIntelligence #TensorFlow #PyTorch #Pandas

1/16

#DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Math #Data #DataAnalytics #pythoncode #AI #ArtificialIntelligence #TensorFlow #PyTorch #Pandas

2/16

"roc_auc_score" is defined as the area under the ROC curve, which is the curve having False Positive Rate on the x-axis and True Positive Rate on the y-axis at all classification thresholds.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python

"roc_auc_score" is defined as the area under the ROC curve, which is the curve having False Positive Rate on the x-axis and True Positive Rate on the y-axis at all classification thresholds.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python

3/16

AUC ranges in value from 0 to 1.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Math #Data #DataAnalytics #pythoncode #AI #ArtificialIntelligence #TensorFlow #PyTorch #Pandas #Stat #dataviz #learning

AUC ranges in value from 0 to 1.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Math #Data #DataAnalytics #pythoncode #AI #ArtificialIntelligence #TensorFlow #PyTorch #Pandas #Stat #dataviz #learning

What is p-value - A thread

It tells you how likely it is that your data could have occurred under the null hypothesis.

1/n

#DataScience #DeepLearning #MachineLearning #ComputerVision #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Data #Math #Stat

It tells you how likely it is that your data could have occurred under the null hypothesis.

1/n

#DataScience #DeepLearning #MachineLearning #ComputerVision #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Data #Math #Stat

2/n

What Is a Null Hypothesis?

A null hypothesis is a type of statistical hypothesis that proposes that no statistical significance exists in a set of given observations.

#DataScience #MachineLearning #100DaysOfMLCode #Python #stat #Statistics #Data #AI #Math #deeplearning

What Is a Null Hypothesis?

A null hypothesis is a type of statistical hypothesis that proposes that no statistical significance exists in a set of given observations.

#DataScience #MachineLearning #100DaysOfMLCode #Python #stat #Statistics #Data #AI #Math #deeplearning

3/n

A P-value is the probability of obtaining an effect at least as extreme as the one in your sample data, assuming the truth of the null hypothesis

#DataScience #MachineLearning #100DaysOfMLCode #Python #DataScientist #Statistics #Data #DataAnalytics #AI #Math

A P-value is the probability of obtaining an effect at least as extreme as the one in your sample data, assuming the truth of the null hypothesis

#DataScience #MachineLearning #100DaysOfMLCode #Python #DataScientist #Statistics #Data #DataAnalytics #AI #Math

It has been an honour to walk alongside my friend & colleague of 21 years @BinitaKane & her daughter Jasmin on their courageous & inspiring journey. #LongCovid in adults children is real & serious. There are treatments available- sadly only for a fortunate few #TeamClots 1/n

(Everything I say is with the express permission of @BinitaKane). Jasmin is not the only child with #LongCovid who has been shown to have microclots & hyperactivated platelets. They are a consistent finding in kids & adults who have travelled for treatment to Germany & SA 2/n

Globally 100m are affected by this vile illness. In the U.K. alone this figure is estimated to be 1.8m, with 2/3rds reporting an adverse impact on their daily activities. 1% of primary & 2.7% of secondary school children fulfilled the criteria for #LongCovidKids 3/n

The British Association of Social Workers have published Fabricated & Induced Illness & Perplexing Presentations Practice Guide basw.co.uk/resources/fabr…

(PP ~ MUS) 🧵1/20

@doctorasadkhan @NurseDiane2020 @PandasPans @LongCovidKids @FiGullonScott @BASW_UK

(PP ~ MUS) 🧵1/20

@doctorasadkhan @NurseDiane2020 @PandasPans @LongCovidKids @FiGullonScott @BASW_UK

This well-considered & positive document provides guidance to social workers (SW) to enable them to adopt professional curiosity & respond ethically to FII concerns. The following points are some that I feel are relevant to #PANS #PANDAS #LongCovid #MECFS #EDS communities 2/20

lt is recommended SW need to be aware of the lack of evidence for currently used indicators for FII & perplexing presentations and the high incidence of these indicators identifying children where illness is neither fabricated or induced #PANS #PANDAS #MECFS #EDS #LongCovid 3/20

[Data Analysis] 🧵

Exploratory data analysis is a fundamental step in any analysis work. You don't have to be a data scientist and be proficient at modeling to be a useful asset to your client if you can do great EDA.

Here's a template of a basic yet powerful EDA workflow👇

Exploratory data analysis is a fundamental step in any analysis work. You don't have to be a data scientist and be proficient at modeling to be a useful asset to your client if you can do great EDA.

Here's a template of a basic yet powerful EDA workflow👇

EDA is incredibly useful. Proper modeling CANNOT happen without it.

The truth:

Stakeholders NEED it far more than modeling.

EDA empowers the analyst with knowledge about the data, which then moderates the #machinelearning pipeline

The truth:

Stakeholders NEED it far more than modeling.

EDA empowers the analyst with knowledge about the data, which then moderates the #machinelearning pipeline

While #pandas and #matplotlib are key to good EDA in #python, the real difference are the QUESTIONS you ask to your dataset.

As in all things, these tools are just tools. The real weapon is the analyst. You are in control, not the dataset.

As in all things, these tools are just tools. The real weapon is the analyst. You are in control, not the dataset.

A thread on AUC Score (Area under the ROC Curve) Interpretation in #DataScience #MachineLearning

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#DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Math #Data #DataAnalytics #pythoncode #AI #ArtificialIntelligence #TensorFlow #PyTorch #Pandas

1/16

#DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Math #Data #DataAnalytics #pythoncode #AI #ArtificialIntelligence #TensorFlow #PyTorch #Pandas

2/16

"roc_auc_score" is defined as the area under the ROC curve, which is the curve having False Positive Rate on the x-axis and True Positive Rate on the y-axis at all classification thresholds.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python

"roc_auc_score" is defined as the area under the ROC curve, which is the curve having False Positive Rate on the x-axis and True Positive Rate on the y-axis at all classification thresholds.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python

3/16

AUC ranges in value from 0 to 1.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Math #Data #DataAnalytics #pythoncode #AI #ArtificialIntelligence #TensorFlow #PyTorch #Pandas #Stat #dataviz #learning

AUC ranges in value from 0 to 1.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Math #Data #DataAnalytics #pythoncode #AI #ArtificialIntelligence #TensorFlow #PyTorch #Pandas #Stat #dataviz #learning

I realized today one of the reasons I appreciate working for a system integrator. Last time this year I was training in #Scala and #Spark. Then, I was placed in a project using #python, #pandas, #pyspark and #sql. That ended 12/31/21 and I had a short break before joining...

a pilot project working with #terraform, #aws, #boto3 with #python, #informatica, #autosys and #shellScripting. Not to mention the increasing frequency of situations that are like "Here is some technology setup that you have never seen before, here is some rudimentary docs,...

Figure it out in 1 or 2 sprints, provide deliverable artifacts and documentation on the preexisting system AND the new artifacts. Thx, k. Bai!" Oh, and teammates you joined with, they all have a shit-ton of stuff, so don't expect replies to your msgs.

Cue: 1. panic, then...

Cue: 1. panic, then...

Resources for getting started in #DataScience

Where to start? What to read? What to learn? I got you covered.

🧵 See thread below 👇

Where to start? What to read? What to learn? I got you covered.

🧵 See thread below 👇

2/ Roadmap to #datascience

Here's my 4 step process on becoming a data scientist

1. Plan

2. Learn

3. Build

4. Explain

👉 Video

👉 Blog towardsdatascience.com/the-art-of-lea…

Here's my 4 step process on becoming a data scientist

1. Plan

2. Learn

3. Build

4. Explain

👉 Video

👉 Blog towardsdatascience.com/the-art-of-lea…

3/ Create your own #datascience learning curriculum

Everyone's interest or needs are different. Therefore the destiny of everyone's learning journey is also different.

Create your own curriculum. Here's how:

👉 Video

👉 Blog towardsdatascience.com/how-to-create-…

Everyone's interest or needs are different. Therefore the destiny of everyone's learning journey is also different.

Create your own curriculum. Here's how:

👉 Video

👉 Blog towardsdatascience.com/how-to-create-…

Want a #Python #pandas data frame with all Apollo missions, indexed by date?

df = pd.read_html('https://t.co/1OfUmAGe6N')[2]

df['Date'] = pd.to_datetime(df['Date'].str.replace('(–.+)?,', '', regex=True))

df = df.set_index('Date')

df = pd.read_html('https://t.co/1OfUmAGe6N')[2]

df['Date'] = pd.to_datetime(df['Date'].str.replace('(–.+)?,', '', regex=True))

df = df.set_index('Date')

The first line scrapes the Wikipedia page for the Apollo program, putting all HTML tables into data frames. The missions are in the third table, aka index 2.

The second line turns lines containing date ranges into single (launch) dates, also removing commas and hyphens.

The second line turns lines containing date ranges into single (launch) dates, also removing commas and hyphens.

That second line then takes the resulting cleaned-up date strings, and passes them to pd.to_datetime. The resulting datetime series is then assigned back to df['Date'].

Quick Thread : 5 Cool Advanced Pandas Techniques for Data Scientists

🧵👇🏻

#Python #DataScience #MachineLearning #DataScientist #Programming #Coding #100DaysofCode #hubofml #Pandas

🧵👇🏻

#Python #DataScience #MachineLearning #DataScientist #Programming #Coding #100DaysofCode #hubofml #Pandas

Some super-useful magic commands for Jupyter notebook - A thread

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#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #DataScientist #Datavisualization #programming #DataAnalytics #pythoncode #pythoncode #AI #pythonprogramming #pythonlearning #Data

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#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #DataScientist #Datavisualization #programming #DataAnalytics #pythoncode #pythoncode #AI #pythonprogramming #pythonlearning #Data

2/n

Types of magic commands

Line magics - starts with % character. Rest of the line is its argument passed without parentheses or quotes.

Cell magics - %% - can operate on multiple lines below their call.

#DataScience #MachineLearning #100DaysOfMLCode #Python #DataScientist

Types of magic commands

Line magics - starts with % character. Rest of the line is its argument passed without parentheses or quotes.

Cell magics - %% - can operate on multiple lines below their call.

#DataScience #MachineLearning #100DaysOfMLCode #Python #DataScientist

3/n

%load - load code from an external source into a cell in Jupyter Notebook

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #DataScientist #Datavisualization #programming #DataAnalytics #pythoncode #pythoncode #AI #pythonprogramming #pythonlearning

%load - load code from an external source into a cell in Jupyter Notebook

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #DataScientist #Datavisualization #programming #DataAnalytics #pythoncode #pythoncode #AI #pythonprogramming #pythonlearning

A thread - All the basic #Matrix #Algebra you will need in #MachineLearning #DeepLearning

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#DataScience #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Math #Data #DataAnalytics #pythoncode #AI #ArtificialIntelligence #TensorFlow #PyTorch #Pandas #Stat

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#DataScience #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Math #Data #DataAnalytics #pythoncode #AI #ArtificialIntelligence #TensorFlow #PyTorch #Pandas #Stat

2/n

A matrix A is a rectangular array of scalars usually presented in the following form

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics #Math #Data #AI #TensorFlow #programming #DataAnalytics #ArtificialIntelligence #Mathematics

A matrix A is a rectangular array of scalars usually presented in the following form

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics #Math #Data #AI #TensorFlow #programming #DataAnalytics #ArtificialIntelligence #Mathematics

A thread on AUC Score Interpretation

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#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Math #Data #DataAnalytics #pythoncode #AI #ArtificialIntelligence #TensorFlow #PyTorch #Pandas #Stat #dataviz #learning

1/16

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Math #Data #DataAnalytics #pythoncode #AI #ArtificialIntelligence #TensorFlow #PyTorch #Pandas #Stat #dataviz #learning

2/16

roc_auc_score is defined as the area under the ROC curve, having False Positive Rate on the x-axis and True Positive Rate on the y-axis at all classification thresholds

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics #Math

roc_auc_score is defined as the area under the ROC curve, having False Positive Rate on the x-axis and True Positive Rate on the y-axis at all classification thresholds

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics #Math

What is p-value

#DataScience #DeepLearning #MachineLearning #ComputerVision #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Data #DataAnalytics #pythoncode #AI #numpy #ArtificialIntelligence #PyTorch #TensorFlow #Pandas #programming #Math #Stat #dataviz

#DataScience #DeepLearning #MachineLearning #ComputerVision #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Data #DataAnalytics #pythoncode #AI #numpy #ArtificialIntelligence #PyTorch #TensorFlow #Pandas #programming #Math #Stat #dataviz

A P-value is the probability of obtaining an effect at least as extreme as the one in your sample data, assuming the truth of the null hypothesis

#DataScience #MachineLearning #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Data #DataAnalytics #AI #Math

#DataScience #MachineLearning #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Data #DataAnalytics #AI #Math

P values address only one question:

How likely are your data, assuming a true null hypothesis ?

#DataScience #MachineLearning #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Data #DataAnalytics #AI #Math #Stat #Python #learning #LearnToCode

How likely are your data, assuming a true null hypothesis ?

#DataScience #MachineLearning #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Data #DataAnalytics #AI #Math #Stat #Python #learning #LearnToCode

Pandas is a fast, powerful, flexible and open source data analysis and manipulation tool.

A Mega thread 🧵covering 10 amazing Pandas hacks and how to efficiently use it(with Code Implementation)👇🏻

#Python #DataScientist #Programming #MachineLearning #100DaysofCode #DataScience

A Mega thread 🧵covering 10 amazing Pandas hacks and how to efficiently use it(with Code Implementation)👇🏻

#Python #DataScientist #Programming #MachineLearning #100DaysofCode #DataScience

1/ #Pandas is the go-to library that you need for #datawrangling for your #datascience projects when coding in #Python.

👀🧵👇 See thread below

👀🧵👇 See thread below

2/ Why Do We Need Pandas?

The Pandas library has a large set of features that will allow you to perform tasks from the first intake of raw data, its cleaning and transformation to the final curated form in order to validate hypothesis testing and machine learning model building.

The Pandas library has a large set of features that will allow you to perform tasks from the first intake of raw data, its cleaning and transformation to the final curated form in order to validate hypothesis testing and machine learning model building.

3/ Basics of Pandas - 1. Pandas Objects

Pandas allows us to work with tabular datasets. The basic data structures of Pandas that consists of 3 types: Series, DataFrame and DataFrameIndex. The first 2 are data structures while the latter serves as a point of reference.

Pandas allows us to work with tabular datasets. The basic data structures of Pandas that consists of 3 types: Series, DataFrame and DataFrameIndex. The first 2 are data structures while the latter serves as a point of reference.

Want to become a Data Scientist? Here are some great resources that you should watch in order;Statistics & Linear Algebra Not Included 🧵

#100DaysOfCode #100DaysOfMLCode #DataScience #AI #MachineLearning #ArtificialIntelligence #ML #Coding #Coders #PyTorch #Tensorflow #Developer

#100DaysOfCode #100DaysOfMLCode #DataScience #AI #MachineLearning #ArtificialIntelligence #ML #Coding #Coders #PyTorch #Tensorflow #Developer

Part 1 of Microsoft's Intro to Python Series:

Gets you up and running in python and introduces you to the basics of setting up your development environment.

youtube.com/playlist?list=…

Gets you up and running in python and introduces you to the basics of setting up your development environment.

youtube.com/playlist?list=…

Continuation of the series above.

youtube.com/playlist?list=…

youtube.com/playlist?list=…

Will Slovakia avoid „Czech scenario“?

Health minister #marekkrajčí 16.10. first time publicly compared the second wave COVID-19 trends in Slovakia and Czech republic in a chart showing that Slovakia is following the scenario in Czech republic with a delay of 2~3weeks.:

Health minister #marekkrajčí 16.10. first time publicly compared the second wave COVID-19 trends in Slovakia and Czech republic in a chart showing that Slovakia is following the scenario in Czech republic with a delay of 2~3weeks.:

Trend chart from few days ago (22.10.) was even worse and clearly showing that Slovakia is repeating the Czech scenario:

Somewhere here we should be looking for motivation of the government in Slovakia and prime minister #igormatovič to solve the problem of rising second wave of epidemics and dark vision of collapsing health care by unusual way - #masstesting in combination with limited lockdown.

Jensen Huang, @nvidia CEO just kicked off #GTC2020 with a keynote that covered several groundbreaking announcements and partnerships. The future of #AI and #Nvidia is incredibly exciting. Here's a thread of the (many) key takeaways. Let's get started!🧵👇

The main focus this time was on #AI & high performance computing in the data center & on the edge. This #GTC2020 Nvidia is releasing 80 new and updated SDKs. CUDA, Nvidia's toolkit for GPU powered applications, has been downloaded 20M times, 6M in 2020 alone.

#Omniverse, Nvidia's platform for simulation & collaboration is now in open beta. Using Omniverse teams can simultaneously work in Blender, Maya, Unreal etc and view the results rendered in real time in a common interface.

The Pandya dynasty was one of the greatest South Indian kingdoms. But what made them so great?

The main reason for the greatness of Pandyas were their conquests. They almost conquered almost all of the South India and earned respect from all over.

The main reason for the greatness of Pandyas were their conquests. They almost conquered almost all of the South India and earned respect from all over.

Pandyan kings were smart enough to choose Madurai as their capital. Madurai had lots of rocks for buildings and was also rich in pearls. This allowed Pandya kingdom to trade all across the world.

Another way the Pandya empire gained power was through their architecture.

Another way the Pandya empire gained power was through their architecture.

Please help us welcome our next curator Darryl Takudzwa Griffiths. @BlaqNinja completed his Bachelors Degree in Computer Engineering at DUT, graduated in 2011. Due to struggling to find suitable employment he went on to study multiple certificates from bodies such as Microsoft.

He has certificates in N+ (Computer Networking), A+ (Computer Technician & Technical Support), Certified Ethical Hacking V7 (CEH v7), Offensive Security Certified Professional (OSCP). Sadly even with these, he could not secure his desired post so in 2016 he moved to USA.

Darryl was able to secure a job in a corporation that owns casinos as a system analyst & security architect. Within the same year he embarked on a Masters degree in Robotics & Artificial Intelligence Engineering. In 2017 he resigned from his post and started his own company...

How to move towards Quantitative Trading :

1) Learn Python

2) Learn key trading libraries & Pandas

3) Data visualisation using seaborn & matplotlib

3) Statistics for Financial Markets

4) Backtest your strategies

5)Optimise & Automate

6) ML insights for trading

#quant #trading

1) Learn Python

2) Learn key trading libraries & Pandas

3) Data visualisation using seaborn & matplotlib

3) Statistics for Financial Markets

4) Backtest your strategies

5)Optimise & Automate

6) ML insights for trading

#quant #trading

1) This is a good basic course on Udemy to learn about Python required for trading

udemy.com/course/python-…

#python

udemy.com/course/python-…

#python

2) Learn about Pandas( cruciwl for data cruncing & handling time series data)

learnpython.org/en/Pandas_Basi…

Also install talib: an essential library for a trader. It has inbuilt functions for all technical indicators making our life easy.

mrjbq7.github.io/ta-lib/doc_ind…

#pandas

learnpython.org/en/Pandas_Basi…

Also install talib: an essential library for a trader. It has inbuilt functions for all technical indicators making our life easy.

mrjbq7.github.io/ta-lib/doc_ind…

#pandas

I used Matlab for image processing for years. Tried to switch to Python 10 years ago but too many tools were still missing. Tried again 5 years ago and haven't touched Matlab ever since! The combination scikit-image + @ProjectJupyter was a real game-changer! A few more things:

On top of the great classics scientific stack (#numpy, #scipy, #pandas, #matplotlib) there's an entire ecosystem of new tools to handle all sorts of complex problems. E.g. #napari to visualize and annotate multi-dimensional data. @dask_dev to handle very large images.

Complex ML tools for image denoising like content-aware image restoration #CARE (github.com/csbdeep/csbdeep) or point-scanning super-resolution #PSSR (github.com/BPHO-Salk/PSSR) which are documented as Jupyter notebooks that really work "out of the box".