CLIP is a bridge between computer vision and natural language processing. I'm here to break CLIP down for you in an accessible and fun read! In this post, I'll cover what CLIP is, how CLIP works, and why CLIP is cool. | Continue reading
We have compiled a list of the top free resources to help new data practitioners learn SQL. These include free online courses and resources to get the most out of your SQL skills. | Continue reading
The first part covers the list of Programming, Web scraping, Data Analytics, SQL, Business Intelligence, and Time Series projects. | Continue reading
A year ago, Objectiv started a community of 50 companies to develop a Hugging Face like open-source project for customer data modeling. They key objective: enable building data models on one team/company’s dataset, and then run them seamlessly on another. | Continue reading
Learn various techniques to reduce data processing time by using multiprocessing, joblib, and tqdm concurrent. | Continue reading
Striving for a new generic way to structure analytics data, so models built on one data set can be deployed and run on another. | Continue reading
Learn how to tune a large language model. | Continue reading
After 28+ years of publishing and editing KDnuggets, I am retiring and transitioning KDnuggets to Matthew Mayo, who will become the new editor-in-chief. I want to share with you my story of KDnuggets and highlight some of the useful nuggets of experience I learned along this amaz … | Continue reading
Check out this curated collection for new and popular tools to add to your data stack this year. | Continue reading
In this article we are discussing that HDF5 is one of the most popular and reliable formats for non-tabular, numerical data. But this format is not optimized for deep learning work. This article suggests what kind of ML native data format should be to truly serve the needs of mod … | Continue reading
If you are working with big data, especially on your local machine, then learning the basics of Vaex, a Python library that enables the fast processing of large datasets, will provide you with a productive alternative to Pandas. | Continue reading
Ever larger models churning on increasingly faster machines suggest a potential path toward smarter AI, such as with the massive GPT-3 language model. However, new, more lean, approaches are being conceived and explored that may rival these super-models, which could lead to a fut … | Continue reading
Check out this helpful, 5-page data science cheat sheet to assist with your exam reviews, interview prep, and anything in-between. | Continue reading
GitHub's Copilot code generation tool is currently only available via approved request. Here are 4 Copilot alternatives that you can use in your programming today. | Continue reading
In this article, we’ll cover a few of the most interesting — and powerful — of these techniques — focusing specifically on semantic search. We’ll learn how they work, what they’re good at, and how we can implement them ourselves. | Continue reading
The way you think about a problem and the conceptual process you go through to find a solution may be guided by your personal skills or the type of problem at hand. Many mental models exist representing a variety of thinking patterns -- and as a Data Scientist, appreciating diffe … | Continue reading
AI-powered products that are limited to the data available within its application are like jellyfish: its autonomic system makes it functional, but it lacks a brain. However, you can evolve your models with data enriched "brains" through the help of a feature store. | Continue reading
Effective and collaborative communication with stakeholders is a skill that can help you survive in your role as a Data Scientist at your organization. Learn how to master this interaction, and you will perform your job better, see improved outcomes from your projects, and grow i … | Continue reading
Embedding models convert raw data such as text, images, audio, logs, and videos into vector embeddings (“vectors”) to be used for predictions, comparisons, and other cognitive-like functions. | Continue reading
AutoML frameworks are getting better every day, and can provide high-performing ML pipelines, unique data insights, and ML explanations. No longer black-boxes, these powerful tools offer self-documenting capabilities and native Python notebook support. | Continue reading
Linear algebra is the branch of mathematics that studies vector spaces. You’ll see how vectors constitute vector spaces and how linear algebra applies linear transformations to these spaces. You’ll also learn the powerful relationship between sets of linear equations and vector e … | Continue reading
In the context of machine learning, some of the concepts of information theory are used to characterize or compare probability distributions. Read up on the underlying math to gain a solid understanding of relevant aspects of information theory. | Continue reading
Delivering machine learning solutions is so much more than the model. Three key concepts covering version control, testing, and pipelines are the foundation for machine learning operations (MLOps) that help data science teams ship models quicker and with more confidence. | Continue reading
This article compares the performance of the well-known pandas library with pypolars, a rising DataFrame library written in Rust. See how they compare. | Continue reading
Check out these 10 books that can help data scientists and aspiring data scientists learn machine learning today. | Continue reading
NLP and deep learning continue to advance, nearly on a daily basis. Check out these recent must-read guides, feature articles, and other resources to keep you on top of the latest advancements and ahead of the curve. | Continue reading
The article addresses a simple data analytics problem, comparing a Python and Pandas solution to an R solution (using plyr, dplyr, and data.table), as well as kdb+ and BigQuery solutions. Performance improvement tricks for these solutions are then covered, as are parallel/cluster … | Continue reading
Data pre-processing is not only the largest time sink for most Data Scientists, but it is also the most crucial aspect of the work. Learn more about training data and data processing tasks from 5 leading academic papers. | Continue reading
This article will highlight some of that research and introduce five machine learning papers on face recognition. | Continue reading
Traditional business and technology sectors are not the only fields being impacted by AI. Healthcare is a field that is thought to be highly suitable for the applications of AI tools and techniques. | Continue reading
AI tools and services are expanding at a rapid clip, and keeping a handle on this evolving ecosystem is crucial for the success of your AI projects. This framework will help you build your technical stack to deploy AI projects faster and at scale. | Continue reading
Getting ready to leap into the world of Data Science? Consider these top machine learning courses curated by experts to help you learn and thrive in this exciting field. | Continue reading
With many organizations having machine learning models running in production, some are discovering that inefficiencies exists in the first step of the process: feature definition and extraction. Robust feature management is now being realized as a key missing part of the ML stack … | Continue reading
Developing machine learning predictive models from time series data is an important skill in Data Science. While the time element in the data provides valuable information for your model, it can also lead you down a path that could fool you into something that isn't real. Follow … | Continue reading
We have compiled a list of some of the best (and free) machine learning books that will prove helpful for everyone aspiring to build a career in the field. | Continue reading
This list will feature some of the recent work and discoveries happening in machine learning, as well as guides and resources for both beginner and intermediate data scientists. | Continue reading
This post will address the issues that can arise when Pandas slicing is used improperly. If you see the warning that reads "A value is trying to be set on a copy of a slice from a DataFrame", this post is for you. | Continue reading
From network security to financial fraud, anomaly detection helps protect businesses, individuals, and online communities. To help improve anomaly detection, researchers have developed a new approach called MIDAS. | Continue reading
The Gartner 2020 Magic Quadrant for Data Science and Machine Learning Platforms has the largest number of leaders ever. We examine the leaders and changes and trends vs previous years. | Continue reading
Soon after tech giants Google and Microsoft introduced their AutoML services to the world, the popularity and interest in these services skyrocketed. We first review AutoML, compare the platforms available, and then test them out against real data scientists to answer the questio … | Continue reading
Follow this overview of Natural Language Generation covering its applications in theory and practice. The evolution of NLG architecture is also described from simple gap-filling to dynamic document creation along with a summary of the most popular NLG models. | Continue reading
Preparing for a job interview can be a full-time job, and Data Science interviews are no different. Here are 121 resources that can help you study and quiz your way to landing your dream data science job. | Continue reading
Google has started offering a new service for “explainable AI” or XAI, as it is fashionably called. Presently offered tools are modest, but the intent is in the right direction. | Continue reading
Such as the gross exaggerations Stanford researchers broadcasted about their infamous "AI gaydar" project, there exists a prevalent "accuracy fallacy" in relation to AI from the media. Find out more about how the press constantly misleads the public into believing that machine le … | Continue reading
With artificial intelligence and machine learning now a mainstay of our daily awareness, news organizations have been seen to overstate the reality behind progress in the field. Learn more about recent examples of media hyperbole and explore why this may be happening. | Continue reading
In this post, learn how to extend Scikit-learn code to make your experiments easier to maintain and reproduce. | Continue reading
In this crash course on GANs, we explore where they fit into the pantheon of generative models, how they've changed over time, and what the future has in store for this area of machine learning. | Continue reading