Original research, concise AI briefs, startup ideas, industry use cases, tutorials, and informed perspectives.
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Computer Vision model for livestream frame people counter. Using OpenCV, YOLO5 from ultralytics, pytorch YOLO5 used the COCO image dataset for training its model Logic: The OpenCV library…
Sharing a few articles related to #AI and #Robotics in #Restaurant #Food #Beverage industry. There is a demand for #automation in this industry due to various factors like…
OpenAI is a well known AI 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…
The role of artificial intelligence in golf club design While we know of many business applications of AI, the field of sports has been using data analytics for…
Interesting article on automation in farming. Big area of interest across the world as labour, supply chain is getting disrupted due to various reasons. Also there is a…
Interesting article on data engineers vs data scientists from https://gradientflow.com/ is given below. One of the main issues for building ML/AI/DL models is availability of good, clean, relevant…
In this article I’d like to share the original paper related to the concept of #Transformers in #NLP for #AI #DeepLearning. It is fundamental to the creation of…
USDA (United States Department of Agriculture) has published a series of articles and documents on how it plans to use AI to create the next generation agriculture food…
This article is comparing the results of using a deep learning algorithm, LSTMs, for a regression problem versus simple Neural Networks and ensemble algorithms like Random Forest and…
This 10% #selloff is just the beginning. Even a 50% #correction would only get to pre #covid levels, when #global #economy was doing fine. #Valuations were #crazy stupid.…
I am sharing this article to show some possible uses for AI/Deep Learning in healthcare industry. Healthcare industry has traditionally been slow at adopting new technologies by nature…
Building a #datascience #machinelearning #algorithm is relatively simple. 8 lines of #python code. It’s the other process steps that require most of the work – problem definition, #datawrangling,…
