- A Breakdown of Deep Learning Frameworks - Sep 23, 2021.
Deep Learning continues to evolve as one of the most powerful techniques in the AI toolbox. Many software packages exist today to support the development of models, and we highlight important options available with key qualities and differentiators to help you select the most appropriate for your needs.
- 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.
- 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.
- 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.
- Data Science for Managers: Programming Languages - Nov 19, 2019.
In this article, we are going to talk about popular languages for Data Science and briefly describe each of them.
- Who is a typical Data Scientist in 2019? - Mar 11, 2019.
We investigate what a typical data scientist looks like and see how this differs from this time last year, looking at skill set, programming languages, industry of employment, country of employment, and more.
- 50+ Data Science, Machine Learning Cheat Sheets, updated - Dec 14, 2016.
Gear up to speed and have concepts and commands handy in Data Science, Data Mining, and Machine learning algorithms with these cheat sheets covering R, Python, Django, MySQL, SQL, Hadoop, Apache Spark, Matlab, and Java.
- 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.
- Machine Learning Course for R&D Specialists, 4-8 April, Delft, The Netherlands - Feb 1, 2016.
Do you want to go beyond theory and learn how to create working Machine Learning solutions? This 5-day course provides you with practical step-by-step methodology.
- 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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- Lund University Develops an Artificial Neural Network for Matching Heart Transplant Donors with Recipients - Jul 9, 2015.
Finding the correct donor for the transplant has been challenging and intensively researched usecase in data science. Here, you can find how MathWorks was used to resolve this problem.
- Top KDnuggets tweets, Oct 3-5: Best Programming Languages for Machine Learning; Analyzing Ebola - Oct 6, 2014.
Best Programming Language for Machine Learning: R, Python, MATLAB - when yo use what; Analyzing Ebola - Is it spreading at exponential rate?; 31,000 people/hour are joining the new private social network Ello; Booking: Data Scientist.