Search results for acid
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7 Best Cloud Database Platforms
Cloud databases have made it easier and cheaper to develop enterprise-level applications, offering flexibility, convenience, and standard database functionality. See what KDnuggets recommends.https://www.kdnuggets.com/7-best-cloud-database-platforms
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Getting Started with SQL in 5 Steps
This comprehensive SQL tutorial covers everything from setting up your SQL environment to mastering advanced concepts like joins, subqueries, and optimizing query performance. With step-by-step examples, this guide is perfect for beginners looking to enhance their data management skills.https://www.kdnuggets.com/5-steps-getting-started-with-sql
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Introduction to Databases in Data Science
Understand the relevance of databases in data science. Also learn the fundamentals of relational databases, NoSQL database categories, and more.https://www.kdnuggets.com/introduction-to-databases-in-data-science
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OLAP vs. OLTP: A Comparative Analysis of Data Processing Systems
A comprehensive comparison between OLAP and OLTP systems, exploring their features, data models, performance needs, and use cases in data engineering.https://www.kdnuggets.com/2023/08/olap-oltp-comparative-analysis-data-processing-systems.html
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Ten Years of AI in Review
From image classification to chatbot therapy.https://www.kdnuggets.com/2023/06/ten-years-ai-review.html
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Principal Component Analysis (PCA) with Scikit-Learn
Learn how to perform principal component analysis (PCA) in Python using the scikit-learn library.https://www.kdnuggets.com/2023/05/principal-component-analysis-pca-scikitlearn.html
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25 Advanced SQL Interview Questions for Data Scientists
Check out this collection of advanced SQL interview questions with answers.https://www.kdnuggets.com/2022/10/25-advanced-sql-interview-questions-data-scientists.html
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Key-Value Databases, Explained
Among the four big NoSQL database types, key-value stores are probably the most popular ones due to their simplicity and fast performance. Let’s further explore how key-value stores work and what are their practical uses.https://www.kdnuggets.com/2021/04/nosql-explained-understanding-key-value-databases.html
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How to Select Rows and Columns in Pandas Using [ ], .loc, iloc, .at and .iat
Subset selection is one of the most frequently performed tasks while manipulating data. Pandas provides different ways to efficiently select subsets of data from your DataFrame.https://www.kdnuggets.com/2019/06/select-rows-columns-pandas.html
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Database Key Terms, Explained
Interested in a survey of important database concepts and terminology? This post concisely defines 16 essential database key terms.https://www.kdnuggets.com/2016/07/database-key-terms-explained.html
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Optimizing Genes with a Genetic Algorithm
In the simplest terms genetic algorithms simulate a population where each individual is a possible “solution” and let survival of the fittest do its thing.https://www.kdnuggets.com/2022/04/optimizing-genes-genetic-algorithm.html
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20 Machine Learning Projects That Will Get You Hired">20 Machine Learning Projects That Will Get You Hired
If you want to break into the machine learning and data science job market, then you will need to demonstrate the proficiency of your skills, especially if you are self-taught through online courses and bootcamps. A project portfolio is a great way to practice your new craft and offer convincing evidence that an employee should hire you over the competition.https://www.kdnuggets.com/2021/09/20-machine-learning-projects-hired.html
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Data Engineering Technologies 2021
Emerging technologies supporting the field of data engineering are growing at a rapid clip. This curated list includes the most important offerings available in 2021.https://www.kdnuggets.com/2021/09/data-engineering-technologies-2021.html
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Math 2.0: The Fundamental Importance of Machine Learning
Machine learning is not just another way to program computers; it represents a fundamental shift in the way we understand the world. It is Math 2.0.https://www.kdnuggets.com/2021/09/math-fundamental-importance-machine-learning.html
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How to Get Practical Data Science Experience to be Career-Ready">How to Get Practical Data Science Experience to be Career-Ready
Becoming a professional in the field of data science takes more than just book-smarts. You need to have experience with real-world data sets, frequently-used tools, and an intuition for solutions that you can only gain from hands-on experience. These resources will jump start developing your practical skills.https://www.kdnuggets.com/2021/07/practical-data-science-experience-career-ready.html
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In-Warehouse Machine Learning and the Modern Data Science Stack
As your organization matures its data science portfolio and capabilities, establishing a modern data stack is vital to enabling such growth. Here, we overview various in-data warehouse machine learning services, and discuss each of their benefits and requirements.https://www.kdnuggets.com/2021/06/in-warehouse-machine-learning-modern-data-science-stack.html
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An introduction to Explainable AI (XAI) and Explainable Boosting Machines (EBM)
Understanding why your AI-based models make the decisions they do is crucial for deploying practical solutions in the real-world. Here, we review some techniques in the field of Explainable AI (XAI), why explainability is important, example models of explainable AI using LIME and SHAP, and demonstrate how Explainable Boosting Machines (EBMs) can make explainability even easier.https://www.kdnuggets.com/2021/06/explainable-ai-xai-explainable-boosting-machines-ebm.html
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DeepMind’s AlphaFold & the Protein Folding Problem
Recently, DeepMind's AlphaFold made impressive headway in the protein structure prediction problem. Read this for an overview and explanation.https://www.kdnuggets.com/2021/03/deepmind-alphafold-protein-folding-problem.html
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Feature Store as a Foundation for Machine Learning
With so many organizations now taking the leap into building production-level machine learning models, many lessons learned are coming to light about the supporting infrastructure. For a variety of important types of use cases, maintaining a centralized feature store is essential for higher ROI and faster delivery to market. In this review, the current feature store landscape is described, and you can learn how to architect one into your MLOps pipeline.https://www.kdnuggets.com/2021/02/feature-store-foundation-machine-learning.html
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Column-Oriented Databases, Explained
NoSQL Databases have four distinct types. Key-value stores, document-stores, graph databases, and column-oriented databases. In this article, we’ll explore column-oriented databases, also known simply as “NoSQL columns”.https://www.kdnuggets.com/2021/02/understanding-nosql-database-types-column-oriented-databases.html
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Fast and Intuitive Statistical Modeling with Pomegranate
Pomegranate is a delicious fruit. It can also be a super useful Python library for statistical analysis. We will show how in this article.https://www.kdnuggets.com/2020/12/fast-intuitive-statistical-modeling-pomegranate.html
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Why the Future of ETL Is Not ELT, But EL(T)">Why the Future of ETL Is Not ELT, But EL(T)
The well-established technologies and tools around ETL (Extract, Transform, Load) are undergoing a potential paradigm shift with new approaches to data storage and expanding cloud-based compute. Decoupling the EL from T could reconcile analytics and operational data management use cases, in a new landscape where data warehouses and data lakes are merging.https://www.kdnuggets.com/2020/12/future-etl-is-elt.html
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A Friendly Introduction to Graph Neural Networks
Despite being what can be a confusing topic, graph neural networks can be distilled into just a handful of simple concepts. Read on to find out more.https://www.kdnuggets.com/2020/11/friendly-introduction-graph-neural-networks.html
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Essential Math for Data Science: Integrals And Area Under The Curve
In this article, you’ll learn about integrals and the area under the curve using the practical data science example of the area under the ROC curve used to compare the performances of two machine learning models.https://www.kdnuggets.com/2020/11/essential-math-data-science-integrals-area-under-curve.html
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Autograd: The Best Machine Learning Library You’re Not Using?">Autograd: The Best Machine Learning Library You’re Not Using?
If there is a Python library that is emblematic of the simplicity, flexibility, and utility of differentiable programming it has to be Autograd.https://www.kdnuggets.com/2020/09/autograd-best-machine-learning-library-not-using.html
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Feature Engineering for Numerical Data
Data feeds machine learning models, and the more the better, right? Well, sometimes numerical data isn't quite right for ingestion, so a variety of methods, detailed in this article, are available to transform raw numbers into something a bit more palatable.https://www.kdnuggets.com/2020/09/feature-engineering-numerical-data.html
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A Deep Learning Dream: Accuracy and Interpretability in a Single Model
IBM Research believes that you can improve the accuracy of interpretable models with knowledge learned in pre-trained models.https://www.kdnuggets.com/2020/09/deep-learning-dream-accuracy-interpretability-single-model.html
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Exploring GPT-3: A New Breakthrough in Language Generation
GPT-3 is the largest natural language processing (NLP) transformer released to date, eclipsing the previous record, Microsoft Research’s Turing-NLG at 17B parameters, by about 10 times. This has resulted in an explosion of demos: some good, some bad, all interesting.https://www.kdnuggets.com/2020/08/exploring-gpt-3-breakthrough-language-generation.html
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Coronavirus COVID-19 Genome Analysis using Biopython">Coronavirus COVID-19 Genome Analysis using Biopython
So in this article, we will interpret, analyze the COVID-19 DNA sequence data and try to get as many insights regarding the proteins that made it up. Later will compare COVID-19 DNA with MERS and SARS and we’ll understand the relationship among them.https://www.kdnuggets.com/2020/04/coronavirus-covid-19-genome-analysis-biopython.html
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2 Things You Need to Know about Reinforcement Learning – Computational Efficiency and Sample Efficiency
Experimenting with different strategies for a reinforcement learning model is crucial to discovering the best approach for your application. However, where you land can have significant impact on your system's energy consumption that could cause you to think again about the efficiency of your computations.https://www.kdnuggets.com/2020/04/2-things-reinforcement-learning.html
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Everything a Data Scientist Should Know About Data Management">Everything a Data Scientist Should Know About Data Management
For full-stack data science mastery, you must understand data management along with all the bells and whistles of machine learning. This high-level overview is a road map for the history and current state of the expansive options for data storage and infrastructure solutions.https://www.kdnuggets.com/2019/10/data-scientist-data-management.html
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A Single Function to Streamline Image Classification with Keras
We show, step-by-step, how to construct a single, generalized, utility function to pull images automatically from a directory and train a convolutional neural net model.https://www.kdnuggets.com/2019/09/single-function-streamline-image-classification-keras.html
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Explore the world of Bioinformatics with Machine Learning">Explore the world of Bioinformatics with Machine Learning
The article contains a brief introduction of Bioinformatics and how a machine learning classification algorithm can be used to classify the type of cancer in each patient by their gene expressions.https://www.kdnuggets.com/2019/09/explore-world-bioinformatics-machine-learning.html
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Automate Stacking In Python: How to Boost Your Performance While Saving Time
Utilizing stacking (stacked generalizations) is a very hot topic when it comes to pushing your machine learning algorithm to new heights. For instance, most if not all winning Kaggle submissions nowadays make use of some form of stacking or a variation of it.https://www.kdnuggets.com/2019/08/automate-stacking-python-boost-performance.html
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A Summary of DeepMind’s Protein Folding Upset at CASP13">A Summary of DeepMind’s Protein Folding Upset at CASP13
Learn how DeepMind dominated the last CASP competition for advancing protein folding models. Their approach using gradient descent is today's state of the art for predicting the 3D structure of a protein knowing only its comprising amino acid compounds.https://www.kdnuggets.com/2019/07/deepmind-protein-folding-upset.html
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The Death of Big Data and the Emergence of the Multi-Cloud Era">The Death of Big Data and the Emergence of the Multi-Cloud Era
The Era of Big Data is coming to an end as the focus shifts from how we collect data to processing that data in real-time. Big Data is now a business asset supporting the next eras of multi-cloud support, machine learning, and real-time analytics.https://www.kdnuggets.com/2019/07/death-big-data-multi-cloud-era.html
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A Beginner’s Guide to Linear Regression in Python with Scikit-Learn
What linear regression is and how it can be implemented for both two variables and multiple variables using Scikit-Learn, which is one of the most popular machine learning libraries for Python.https://www.kdnuggets.com/2019/03/beginners-guide-linear-regression-python-scikit-learn.html
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Introduction to Deep Learning
I decided to begin to put some structure in my understanding of Neural Networks through this series of articles.https://www.kdnuggets.com/2018/09/introduction-deep-learning.html
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Graph Databases Burst into the Mainstream
What do Amazon, Facebook, Google, IBM, Microsoft and Twitter have in common? They're all adopters of graph databases - a hot technology that continues to evolve.https://www.kdnuggets.com/2018/02/graph-databases-burst-into-the-mainstream.html
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Hacking in silico protein engineering with Machine Learning and AI, explained
Proteins are building blocks of all living matter. Although tremendous progress has been made, protein engineering remains laborious, expensive and truly complicated. Here is how Machine Learning can help.https://www.kdnuggets.com/2017/07/hacking-silico-protein-engineering-machine-learning.html
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Data Science and Big Data, Explained">Data Science and Big Data, Explained
This article is meant to give the non-data scientist a solid overview of the many concepts and terms behind data science and big data. While related terms will be mentioned at a very high level, the reader is encouraged to explore the references and other resources for additional detail.https://www.kdnuggets.com/2016/11/big-data-data-science-explained.html
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Evaluating HTAP Databases for Machine Learning Applications
Businesses are producing a greater number of intelligent applications; which traditional databases are unable to support. A new class of databases, Hybrid Transactional and Analytical Processing (HTAP) databases, offers a variety of capabilities with specific strengths and weaknesses to consider. This article aims to give application developers and data scientists a better understanding of the HTAP database ecosystem so they can make the right choice for their intelligent application.https://www.kdnuggets.com/2016/11/evaluating-htap-databases-machine-learning-applications.html
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5 Reasons Machine Learning Applications Need a Better Lambda Architecture
The Lambda Architecture enables a continuous processing of real-time data. It is a painful process that gets the job done, but at a great cost. Here is a simplified solution called as Lambda-R (ƛ-R) for the Relational Lambda.https://www.kdnuggets.com/2016/05/5-reasons-machine-learning-applications-lambda-architecture.html
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Simple Data Science of Global Warming
You don't have to be a climatologist to empirically confirm global warming. It is enough to have a computer, a reliable data set of historical temperatures, and software like R to do simple calculations.https://www.kdnuggets.com/2015/01/data-science-global-warming.html
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16 NoSQL, NewSQL Databases To Watch
NoSQL and NewSQL databases have become much more important with the proliferation of big, mobile, and networked data, and these sixteen database solutions are some of the biggest up-and-comers.https://www.kdnuggets.com/2014/12/16-nosql-newsql-databases-to-watch.html