๐ Learn how to build AI systems for medical imaging domain by leveraging tools and techniques that I share with you! | ๐ก The newsletter is read by people from: Nvidia, Baker Hughes, Harvard, NYU, Columbia University, University of Toronto and more!
Hello Reader,
Welcome to another edition of PYCAD newsletter where we cover interesting topics in Machine Learning and Computer Vision applied to Medical Imaging. The goal of this newsletter is to help you stay up-to-date and learn important concepts in this amazing field! I've got some cool insights for you below โ
How do you generate images from low dimensional vectors?
How do you go from low dimensional data (like a vector of size 100) to higher dimensional data (like an image of size 200x200)?
In many Generative AI applications, youโd need to do this. So how can you do it?
In 2D, you have 2 options:
Whatโs the difference between the two options?
When you use Conv2DTranspose, youโre doing upsampling by adding zeros vertically and horizontally.
When you use UpSampling2D + Conv2D, youโre doing upsampling by duplicating rows and columns.
When to use which?
There is no one answer. It really depends on your experimentation.
One drawback though for Conv2DTranspose, is that sometimes the output generated image can have some checkerboard artifacts like the image below.
A recurrent problem with conda and how to solve it!
Most of us in the field of machine learning are using conda. But most of us face this problem at some point.
How do you create virtual environments on your local disk in a directory other than the default "Anaconda3/envs"?
Since we usually end up installing Anaconda in the default path during the installation process, it ends up being installed in the C drive.
This can quickly fill up the C disk.
So if you decide to create virtual environments and save them in another drive, how can you do that?
Well, you can achieve this by using the argument --๐ฉ๐ซ๐๐๐ข๐ฑ.
To install a new virtual environment in a directory inside D drive for example, you can do this:
๐๐จ๐ง๐๐ ๐๐ซ๐๐๐ญ๐ --๐ฉ๐ซ๐๐๐ข๐ฑ=๐:\๐ฉ๐๐ญ๐ก\...\๐ฒ๐จ๐ฎ๐ซ๐๐ง๐ฏ๐๐๐ฆ๐ ๐ฉ๐ฒ๐ญ๐ก๐จ๐ง=๐ฑ.๐ฑ
One thing you should know is, conda only keeps track of the environments included in the folder envs inside the anaconda folder.
So, the next time you need to activate your new virtual environment, move to the folder where you created it and activate it with:
๐๐จ๐ง๐๐ ๐๐๐ญ๐ข๐ฏ๐๐ญ๐ ๐ฒ๐จ๐ฎ๐ซ๐๐ง๐ฏ๐๐๐ฆ๐
ChatGPT is now available on Azure!
March 9th 2023
|
โ
Advertise with AIFEE to get your brand in front of hundreds of readers who are eager to start or accelerate their machine learning careers by learning about the latest and greatest in machine learning from: books, courses or online communities.
Many of our readers are also looking for machine learning related jobs.
by Nour Islam Mokhtari from pycad.co
๐ Learn how to build AI systems for medical imaging domain by leveraging tools and techniques that I share with you! | ๐ก The newsletter is read by people from: Nvidia, Baker Hughes, Harvard, NYU, Columbia University, University of Toronto and more!
Hi Reader,, Welcome to the PYCAD newsletter, where every week you receive doses of machine learning and computer vision techniques and tools to help you learn how to build AI solutions to empower the most vulnerable members of our society, patients. TotalSegmentator : Whole Body Segmentation at your Fingertips This free tool available online can do full body segmentation, it's called TotalSegmentator. I have already mentioned this tool in a previous edition of the newsletter, but in this...
Hello Reader, Welcome to another edition of PYCAD newsletter where we cover interesting topics in Machine Learning and Computer Vision applied to Medical Imaging. The goal of this newsletter is to help you stay up-to-date and learn important concepts in this amazing field! I've got some cool insights for you below โ A Medical Imaging Expert Told Me This Recently I saw a post on LinkedIn where a medical imaging expert showcased his work of segmenting the lungs and its bronchial trees. You can...
Hello Reader, Welcome to another edition of PYCAD newsletter where we cover interesting topics in Machine Learning and Computer Vision applied to Medical Imaging. The goal of this newsletter is to help you stay up-to-date and learn important concepts in this amazing field! I've got some cool insights for you below โ How we helped accelerate inference time for a client's AI product Below is a screenshot of a benchmark we did for a client of ours. The goal was to accelerate inference time. This...