Discover and read the best of Twitter Threads about #scipy
Most recents (3)
Interested in learning more about #DataScience, #MachineLearning or #AI? I’ve got a few places and resources for medics to start with. Anyone can do it with enough time and effort! Soon enough you’ll be making your own neural networks
1/16. A thread 🧵.
1/16. A thread 🧵.
2/16. Everyone has their preferences with programming languages. However if you’re starting from scratch, I highly recommend #Python. It is easy to learn, has a wide variety of applications and you will find it is much easier to perform even the most basic of statistics.
3/16. It also gives you access to multiple libraries that are used heavily by the machine learning community such as #Keras, #TensorFlow and #PyTorch.
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)
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".
