Analytics Vidhya
OCTOBER 14, 2019
Overview Here’s an intuitive and beginner friendly guide to the mathematics behind machine learning Learn the various math concepts required for machine learning, including. The post Mathematics behind Machine Learning – The Core Concepts you Need to Know appeared first on Analytics Vidhya.
Corinium
OCTOBER 14, 2019
Netflix’s recommendation engine may have made waves when the company’s streaming platform launched in 2010. But in the years that followed, the broadcast industry was surprisingly slow to adopt other AI or machine learning (ML) technologies.
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KDnuggets
OCTOBER 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.
Smart Data Collective
OCTOBER 14, 2019
From the tech industry to retail and finance, big data is encompassing the world as we know it. More organizations rely on big data to help with decision making and to analyze and explore future trends. For current and future software development companies that want to be knowledgeable about using data and analysis, a few big data skillsets will help give them leverage in the coming year.
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Many organizations today are unlocking the power of their data by using graph databases to feed downstream analytics, enahance visualizations, and more. Yet, when different graph nodes represent the same entity, graphs get messy. Watch this essential video with Senzing CEO Jeff Jonas on how adding entity resolution to a graph database condenses network graphs to improve analytics and save your analysts time.
KDnuggets
OCTOBER 14, 2019
Selecting the perfect machine learning model is part art and part science. Learn how to review multiple models and pick the best in both competitive and real-world applications.
Smart Data Collective
OCTOBER 14, 2019
Advances in mass storage and mobile computing brought about the phenomenon we now know as “big data.” These developments then ushered in solutions and tools that can process vast amounts of information — think terabytes of it or more — in real-time. That is how “big” the need for big data analytics came to be. More specifically, big data analytics offers users the ability to generate relevant insights from heaps of data.
Data Leaders Brief brings together the best content for data, strategy, and BI professionals from the widest variety of industry thought leaders.
Jen Stirrup
OCTOBER 14, 2019
As a freelance industry analyst who has worked with GigaOm , I’m pleased to see the GigaOM Transactional Field Test derived from the industry-standard TPC Benchmark E (TPC-E) report which compares Amazon Web Services Relational Database Service (AWS RDS) and Microsoft Azure SQL Database. It’s written by William McKnight and Jake Dolezal from GigaOm.
IBM Big Data Hub
OCTOBER 14, 2019
We recommend Cloud Pak for Data to our clients who are considering machine-learning based Data analytics to get best ROI out of the volumes of data they accumulated over the years.
KDnuggets
OCTOBER 14, 2019
Also: Activation maps for deep learning models in a few lines of code; The 4 Quadrants of Data Science Skills and 7 Principles for Creating a Viral Data Visualization; OpenAI Tried to Train AI Agents to Play Hide-And-Seek but Instead They Were Shocked by What They Learned; 10 Great Python Resources for Aspiring Data Scientists.
DataCamp
OCTOBER 14, 2019
How one power user championed DataCamp and saved his team 196 workdays.
Speaker: Scott Sehlhorst
We know we want to create products which our customers find to be valuable. Whether we label it as customer-centric or product-led depends on how long we've been doing product management. There are three challenges we face when doing this. The obvious challenge is figuring out what our users need; the non-obvious challenges are in creating a shared understanding of those needs and in sensing if what we're doing is meeting those needs.
KDnuggets
OCTOBER 14, 2019
A recent survey outlined the main neural architecture search methods used to automate the design of deep learning systems.
Corinium
OCTOBER 14, 2019
Business Intelligence investment is booming. And, the amount of data available for reporting and analysis is skyrocketing. So, it’s never been harder, or more important, to quickly uncover and communicate the actionable insights within your data. But, how do you separate the gold from the guff, and deliver value from your BI deployment? Download this data visualization best practice guide – and learn how to choose, design and deliver the best visualizations to effectively communicate the signifi
Dataiku
OCTOBER 14, 2019
Roger Magoulas, VP of Radar at O'Reilly Media, Inc., asks: Why is the final mile such a challenge for so many organizations who are working on AI and machine learning? Dataiku Data Scientist Jed Dougherty has answers.
DataRobot
OCTOBER 14, 2019
Database marketing based on the Recency-Frequency-Monetary (RFM) approach has been the standard in casino marketing for many decades. Unfortunately, RFM-based marketing has several limitations for casinos. Potentially profitable players are overlooked, while current players may be trained to continually expect a discount. Meanwhile, a proliferation of alternative entertainment options have emerged and are changing customer behavior, presenting risks to casinos if they fail to change their market
Speaker: Timothy Chan, PhD., Head of Data Science
Are you ready to move beyond the basics and take a deep dive into the cutting-edge techniques that are reshaping the landscape of experimentation? 🌐 From Sequential Testing to Multi-Armed Bandits, Switchback Experiments to Stratified Sampling, Timothy Chan, Data Science Lead, is here to unravel the mysteries of these powerful methodologies that are revolutionizing how we approach testing.
Smart Data Collective
OCTOBER 14, 2019
Regardless of if you’re a data science professional or an IT department who wants to help your company have more successful data science projects, it’s essential to have some data science tools under your belt to avail of when needed. Here are some open-source options to consider. 1. Ludwig. Ludwig is a tool that allows people to build data-based deep learning models to make predictions.
Smarten
OCTOBER 14, 2019
Enjoy the Benefits of Advanced Analytics with Augmented Analytics Support! As a business manager or a business team member, you probably make it your business to stay abreast of industry and market trends and to understand how best to use technology to refine business results and better understand your market, competition and customers. If you have been reading industry publications, you are probably familiar with the concept of augmented analytics and augmented analytics benefits.
Andrew White
OCTOBER 14, 2019
A debate resurfaced in our team recently as we were discussing plans for research topics next year. I am not actually sure why we do such things; the ‘year’ is an artificial thing we created to organize certain activities. Often it is more a strait-jacket that prevents continuous processes like innovation. But I digress. We were discussion ideas and brainstorming how they connect and impact each other, and one of our team said, “The model doesn’t matter to me.
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