Data preparation, statistics, analytical methods, experimentation, and decision support.
This article will discuss the basic theory and relevant code examples for different regression algorithms. It will also show how to test and compare multiple regression algorithms at…
Most data science problems can be solved by following a standard process Process Flow diagram with some details is given below. This is just one of the many…
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…
Using Tensorflow and keras for building CNN model with CIFAR-10 dataset for image classification Built different CNN models using different techniques and layersAchieved an accuracy of more than…
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 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…
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…
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,…
