π Learn how to build AI systems for medical imaging domain by leveraging tools and techniques that I share with you! | π‘ 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.
If you try to build a chatbot based on ChatGPT with your own data, you might face a big issue.
Your chatbot may start to spit out some non sensical responses.
For example, if your chatbot is for a support system for clothing, if a user asks the chatbot about food, it might actually answer him!
You definitely donβt want this.
A more reliable behavior would be:
If a user asks about food, say you donβt have information about that topic.
But how can you have that type of control over LLMs?
This is where NeMo Guardrails comes to the picture.
Itβs an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.
Guardrails (or βrailsβ for short) are specific ways of controlling the output of a large language model.
For example you can have control over:
In the image below, you see a configuration that controls how your Chatbot should respond to an insult from a user!
More info about NeMo Guardrails can be found here.
In traditional software engineering, you have software compilers.
Whatβs the equivalent in deep learning engineering?
Well, deep learning compilers!
A deep learning compiler translates high-level code written in deep learning frameworks into optimized lower level hardware specific code to accelerate training and inference.
Deep learning compilers have to work with highly-parallelizable code often accelerated on specialized AI accelerator hardware such as:
To improve performance, a deep learning compiler has to take advantage of hardware specific features such as:
Pytorch-2.0 has a deep learning compiler. You can invoke it using a single line of code:
torch.compile()
This can yield hardware specific, highly optimized Pytorch models! With a single line of code!
A code example can be seen below β
Here's a full tutorial on Pytorch deep learning compiler.
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by Nour Islam Mokhtari from pycad.co
π Learn how to build AI systems for medical imaging domain by leveraging tools and techniques that I share with you! | π‘ 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 β 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 β 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...