If you want to learn about #bioimageanalysis I've written a free & open textbook that tries to help:

bioimagebook.github.io

Thanks to the wonder of @ExecutableBooks & other modern magic it's not quite like a normal book... (thread)

@OpenEdEdinburgh @NEUBIAS_COST @BioimagingNA

bioimagebook.github.io

Thanks to the wonder of @ExecutableBooks & other modern magic it's not quite like a normal book... (thread)

@OpenEdEdinburgh @NEUBIAS_COST @BioimagingNA

First, the book tries to cover the main concepts, independently of any software, in a practical way.

This includes common pitfalls & problems, like data clipping, that can doom analysis from the start (2/n)

This includes common pitfalls & problems, like data clipping, that can doom analysis from the start (2/n)

It also includes tricky stuff important for a lot of microscopy image analysis, like noise distributions & the signal-to-noise ratio... (3/n)

What's an image made of?

There are many correct answers.

But the most fascinating one is: << sines & cosines >>

Read on if you're intrigued👇🧵🪡

#python #images #fourier

There are many correct answers.

But the most fascinating one is: << sines & cosines >>

Read on if you're intrigued👇🧵🪡

#python #images #fourier

*Any* image can be reconstructed from a series of sinusoidal gratings.

A sinusoidal grating looks like this…

#sinusoidal #grating

A sinusoidal grating looks like this…

#sinusoidal #grating

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

NumPy is a powerful #python library that helps us compute operations on primarily numbers, faster. It is an important tool for data science. 💪

Here are 5 powerful #NumPy functions that will help you in your projects!

#Thread ☕

#MachineLearning #datascience #AI

Let's go! ⬇️

Here are 5 powerful #NumPy functions that will help you in your projects!

#Thread ☕

#MachineLearning #datascience #AI

Let's go! ⬇️

First let’s see the advantages of using NumPy : 📈

1️⃣ Uses low memory to store data.

2️⃣ We can create n-dimensional arrays.

3️⃣ Operations like indexing, broadcasting, slicing and matrix multiplication.

4️⃣ Finding elements in the array is easy.

5️⃣ Good documentation.

1️⃣ Uses low memory to store data.

2️⃣ We can create n-dimensional arrays.

3️⃣ Operations like indexing, broadcasting, slicing and matrix multiplication.

4️⃣ Finding elements in the array is easy.

5️⃣ Good documentation.

Encouraging! #AppleM1 Silicon (MBA) smokes my 2017 MBP15" i7 on #rstats #tidverse tidymodels hotel example, random forests (last fit 100 trees). Experimental arm-R build = extra speedup. Thanks @fxcoudert for gfortran build & @juliasilge @topepos + team for the nice API + DOC.

And it’s wonderful to see that essential R packages are working on the M1 platform.

Another implication might be that 4 cores are a good default for parallel processing with this configuration. The original tidymodels example would select 8 cores here. tidymodels.org/start/case-stu…

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.

If you are using matplotlib in your day to day coding activity and didn't no the crux of it, I want to you take a look at this below thread. I'm sure you will learn and apply it.

So, here is what all we need to know about matplotlib, an excellent visualization library in python

So, here is what all we need to know about matplotlib, an excellent visualization library in python

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".