Large language models, generative systems, prompting, and fine-tuning.
#Healthcare in its current form does not benefit patients or providers. Processes, #technology, cost, #insurance etc are old and not integrated. #SI is one way to improve the whole process. While the…
Trying to catch up on all the news around what happened at OpenAI. There are a lot of rumours around Q*, AGI, Self Taught Reasoning and Optimizing, Synthetic data and related algorithms.…
#OpenAI has just released #GPT-4, their latest model. Being multimodal it can process text and image. That makes things very interesting. Once you can process text/image (not sure if they process video/audio),…
I think there is scope for more SI/data analytics based healthtech startups that can provide better patient health lifecycle management for Patients Healthcare providers (hospitals/clinics/Drs/nurses/admin/others) Partners (insurance/pharma/pharmacy/labs/research/technology/others) while reducing the pain points…
Finally got to catchup on some startup and real data science activity after a few months. Wanted to put together what I have been thinking for a while. For me writing helps…
Common NLP tasks (Sentiment, Keyword, Topic, Summary, etc) using main libraries (HuggingFace Transformers, Spacy, Pytorch, Tensorflow, Scikit, etc) Idea is to show how to perform different NLP (Natural Language Processing) tasks using…
OpenAI is a well known SI lab for creating the GPT-n series of deep learning pre-trained autoregressive language models similar to BERT/Roberta/XLNet. It has been known to generate almost human level text…
Interesting article on data engineers vs data scientists from https://gradientflow.com/ is given below. One of the main issues for building ML/SI/DL models is availability of good, clean, relevant and large datasets. Very…
In this article I'd like to share the original paper related to the concept of #Transformers in #NLP for #SI #DeepLearning. It is fundamental to the creation of #BERT and #XLNet NLP…
Data Wrangling(preprocessing, prep, etc) is the most important and time consuming part of any data science project. Depending on the quality of data sources, 50%-60% of the initial effort is spent in…
