- Industry 2021 Predictions for AI, Analytics, Data Science, Machine Learning - Dec 16, 2020.
We bring you industry predictions from 12 innovative companies - what key trends they expect in 2021 in AI, Analytics, Data Science, and Machine Learning?
- Building AI Models for High-Frequency Streaming Data – Part Two - Dec 10, 2020.
Many data scientists have implemented machine or deep learning algorithms on static data or in batch, but what considerations must you make when building models for a streaming environment? In this post, we will discuss these considerations.
- Building AI Models for High-Frequency Streaming Data - Dec 2, 2020.
This post is the first in a two-part series on AI for streaming data. Here, we’ll walk through strategies for aligning times and resampling the data.
- AI Is More Than a Model: Four Steps to Complete Workflow Success - Nov 17, 2020.
The key element for success in practical AI implementation is uncovering any issues early on and knowing what aspects of the workflow to focus time and resources on for the best results—and it’s not always the most obvious steps.
- MathWorks Deep learning workflow: tips, tricks, and often forgotten steps - Sep 22, 2020.
Getting started in deep learning – and adopting an organized, sustainable, and reproducible workflow – can be challenging. This blog post will share some tips and tricks to help you develop a systematic, effective, attainable, and scalable deep learning workflow as you experiment with different deep learning models, datasets, and applications.
- Leaders, Changes, and Trends in Gartner 2020 Magic Quadrant for Data Science and Machine Learning Platforms - Feb 24, 2020.
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.
- Industry AI, Analytics, Machine Learning, Data Science Predictions for 2020 - Dec 16, 2019.
Predictions for 2020 from a dozen innovative companies in AI, Analytics, Machine Learning, Data Science, and Data industry.
- Moving Predictive Maintenance from Theory to Practice - Dec 9, 2019.
Here are four common hurdles that need to be overcome before tapping into the benefits of predictive maintenance.
- Three Things to Know About Reinforcement Learning - Oct 14, 2019.
As an engineer, scientist, or researcher, you may want to take advantage of this new and growing technology, but where do you start? The best place to begin is to understand what the concept is, how to implement it, and whether it’s the right approach for a given problem.
- Common Machine Learning Obstacles - Sep 9, 2019.
In this blog, Seth DeLand of MathWorks discusses two of the most common obstacles relate to choosing the right classification model and eliminating data overfitting.
- Natural Language Processing Q&A - Jun 24, 2019.
In this Q&A, Jos Martin, Senior Engineering Manager at MathWorks, discusses recent NLP developments and the applications that are benefitting from the technology.
- Gainers, Losers, and Trends in Gartner 2019 Magic Quadrant for Data Science and Machine Learning Platforms - Feb 11, 2019.
We compare Gartner 2019 MQ for Data Science, Machine Learning Platforms to its previous versions and identify notable changes for leaders and challengers, including RapidMiner, KNIME, TIBCO, Alteryx, Dataiku, SAS, and MathWorks.
- Industry Predictions: AI, Machine Learning, Analytics & Data Science Main Developments in 2018 and Key Trends for 2019 - Dec 18, 2018.
This is a collection of data science, machine learning, analytics, and AI predictions for next year from a number of top industry organizations. See what the insiders feel is on the horizon for 2019!
- 3 Challenges for Companies Tackling Data Science - Nov 26, 2018.
From new technology to workflows, we outline three of the more common problems and how businesses can overcome them.
- Top Obstacles to Overcome when Implementing Predictive Maintenance - Oct 29, 2018.
We investigate how to create a systematic approach to predictive maintenance, ensuring there's enough data to create accurate systems. This post also explains how to identify a failure source and knowing how to predict it.
- Top 3 Trends in Deep Learning - Oct 3, 2018.
We investigate the intermediate stage of deep learning, and the trends that are emerging in response to the challenges at this stage, including Interoperability and the multi-deployment options.
- Building a Machine Learning Model through Trial and Error - Sep 24, 2018.
A step-by-step guide that includes suggestions on how to preprocess data and deriving features from this. This article also contains links to help you explore additional resources about machine learning methods and other examples.
- Industry Predictions: Main AI, Big Data, Data Science Developments in 2017 and Trends for 2018 - Dec 19, 2017.
Here is a treasure trove of analysis and predictions from 17 leading companies in AI, Big Data, Data Science, and Machine Learning: What happened in 2017 and what will 2018 bring?
- Gartner 2017 Magic Quadrant for Data Science Platforms: gainers and losers - Feb 23, 2017.
We compare Gartner 2017 Magic Quadrant for Data Science Platforms vs its 2016 version and identify notable changes for leaders and challengers, including IBM, SAS, RapidMiner, KNIME, MathWorks, Microsoft, and Quest.
- Industry Predictions: Key Trends in 2017 - Dec 16, 2016.
With 2017 almost upon us, KDnuggets brings you opinions from industry leaders as to what the relevant and most important 2017 key trends will be.
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- The steps in the machine learning workflow - Jul 28, 2016.
We outline preprocessing steps for finding, removing, and cleaning data to prepare it for machine learning and how tools like MATLAB can help with data exploration, identification of key traits, and communicating the findings.
- Systematic Fraud Detection Through Automated Data Analytics in MATLAB - Aug 27, 2015.
Fraud detection is one of the most challenging use case considering the number of factors it depend on. Here, we demonstrate how using hedge fund data in MATLAB you can automate the process of acquiring and analyzing fraud detection data.
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- Analyzing and Visualizing Flows in Rivers and Lakes with MATLAB - Jul 20, 2015.
ADCPs and VMT have increased the pace of studies that rely on flow data. Find out how these toolkits from MathWorks are revolutionizing the analysis and visualisation processes.
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