Category Archives: Data Science News

This article is a practical guide to exploring any data science project and gain valuable insights. Originally from KDnuggets https://ift.tt/3rjKlNq source https://365datascience.weebly.com/the-best-data-science-blog-2020/11-essential-code-blocks-for-complete-eda-exploratory-data-analysis

Photo by Tierra Mallorca on Unsplash Digital technologies have become an integral part of the house-hunting process, shaping the home buying industry and affecting everyone involved. The research shows that every second buyer heads online first to look for a new home. Responding to this, real estate agents, brokers, and investors are leaning toward custom software andContinue reading “5 Tech Trends Redefining the Home Buying Experience in 2021”

Labeling Service Case Study — Video Annotation — License Plate Recognition Vehicle License Plate Recognition Driven by the continuous innovation of “Ai + IoT” technology and application, a construction boom of “smart city” has been rising all over the world. As a key part of the construction, the intelligent transportation industry has been into the stage of rapid expansion.Continue reading “Labeling Case StudyVideo AnnotationLicense Plate Recognition”

Deploy AI models -Part 3 using Flask and Json We have seen in the earlier part of the AI model deployment how to set up the directory as well as the git. In this part we will build the foundation of how we can deploy model locally and in the next article we deploy the modelContinue reading “Deploy AI models Deployment Part 3. Deploy model locally using Flask and Json”

If your scikit-learn models are taking a bit of time to train, then there are several techniques you can use to make the processing more efficient. From optimizing your model configuration to leveraging libraries to speed up training through parallelization, you can build the best scikit-learn model possible in the least amount of time. OriginallyContinue reading “How to Speed up Scikit-Learn Model Training”

PyCaret, a low code Python ML library, offers several ways to tune the hyper-parameters of a created model. In this post, I’d like to show how Ray Tune is integrated with PyCaret, and how easy it is to leverage its algorithms and distributed computing to achieve results superior to default random search method. Originally fromContinue reading “Bayesian Hyperparameter Optimization with tune-sklearn in PyCaret”

Penn MS of Computer and Information Technology is an online masters degree tailored for non-CS majors, empowering then to succeed in computing and technology fields. Apply by May 1. Originally from KDnuggets https://ift.tt/3sK8DAo source https://365datascience.weebly.com/the-best-data-science-blog-2020/start-a-career-in-computer-science-with-penns-master-in-computer-science-and-information-technology

The increasing computation time and costs of training natural language models (NLP) highlight the importance of inventing computationally efficient models that retain top modeling power with reduced or accelerated computation. A single experiment training a top-performing language model on the ‘Billion Word’ benchmark would take 384 GPU days and as much as $36,000 using AWSContinue reading “Reducing the High Cost of Training NLP Models With SRU”

The Examples and Benefits of AI in Healthcare: From accurate diagnosis to remote patient monitoring 2020 was extremely challenging for healthcare. A year after, the challenges carry through. Caregivers continue to fight against the COVID-19 pandemic — the mission hard enough as it is, to say nothing of its aggravation by a workforce shortage, the national mentalContinue reading “The Examples and Benefits of AI in Healthcare”

The number of resources about Artificial Intelligence (AI) can be overwhelming. If you want to start learning about it, you’ll probably start running across confusing acronyms and terms like ML, NLP, Deep Learning or Reinforcement Learning that will make you wonder why you started learning AI in the first place. But don’t worry, there are 8Continue reading “8 concepts you must know in the field of Artificial Intelligence”