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.
KDnuggets
OCTOBER 16, 2019
A guide covering the things you should learn to become a data scientist, including the basics of business intelligence, statistics, programming, and machine learning.
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Dataconomy
OCTOBER 17, 2019
Which are the AI startups that are innovating in Europe? Why is the world so skeptic about AI innovation in Europe? From where will the next wave of innovation come? Read on. Pundits say that Europe is losing the race for artificial intelligence (AI). Their perspective is suspiciously dire and. The post Do Not Underestimate Europe’s AI Innovators appeared first on Dataconomy.
Hacker News
OCTOBER 13, 2019
This is my advice to anyone writing something for the public — especially a talk on stage. People listen to a talk, or read an article, because they want to learn something new. They want a little “oh wow” moment. “I never thought of it that way before.”. People only really learn when they’re surprised. If they’re not surprised, then what you told them just fits in with what they already know.
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Generative AI is upending the way product developers & end-users alike are interacting with data. Despite the potential of AI, many are left with questions about the future of product development: How will AI impact my business and contribute to its success? What can product managers and developers expect in the future with the widespread adoption of AI?
Analytics Vidhya
OCTOBER 13, 2019
Overview Knowledge graphs are one of the most fascinating concepts in data science Learn how to build a knowledge graph using text from Wikipedia. The post Knowledge Graph – A Powerful Data Science Technique to Mine Information from Text (with Python code) appeared first on Analytics Vidhya.
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.
Data Science Current brings together the best content for data science professionals from the widest variety of thought leaders.
DataRobot
OCTOBER 17, 2019
In the past 12 months, artificial intelligence has become headline news, and not always for the right reasons. We’ve heard stories of sexist AI hiring algorithms , racist face detection algorithms , and AIs using your private social media data to influence elections. Embarrassing AI failures such as these can damage your reputation, incur regulatory penalties, and even negatively impact a company’s stock prices.
Analytics Vidhya
OCTOBER 16, 2019
Overview Swift is quickly becoming one of the most powerful and effective languages for data science Swift is quite similar to Python so you’ll. The post A Comprehensive Guide to Learn Swift from Scratch for Data Science appeared first on Analytics Vidhya.
KDnuggets
OCTOBER 17, 2019
While the average salary for a Software Engineer is around $100,000 to $150,000, to make the big bucks you want to be an AI or Machine Learning (Specialist/Scientist/Engineer.).
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.
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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.
Depends on the Definition
OCTOBER 14, 2019
Last weekendPyCon DE and PyData Berlin joined in Berlin for a great conference event that I was lucky to attend. The speaker line-up was great and often it was hard to choose which talk or tutorial to attend.
Analytics Vidhya
OCTOBER 15, 2019
Overview Qlik is widely associated with powerful dashboards and business intelligence reports Did you know that you can use the power of Qlik to. The post Build your First Linear Regression Model in Qlik Sense appeared first on Analytics Vidhya.
KDnuggets
OCTOBER 17, 2019
This post aims to make you get started with putting your trained machine learning models into production using Flask API.
DataCentric podcast
OCTOBER 14, 2019
As ARM TechCon 2019 wraps up in San Jose, hosts Steve McDowell and Matt Kimball from Moor Insights & Strategy are joined by Patrick Kennedy of Serve the Home in a wide-ranging discussion of the entire microprocessor market, and where ARM fits in. Here's a general timeline for the discussion, though the topics overlap, so you owe it to yourself to listent to the entire thing. 00:00 Intros 02:20 Big Themes & Announcements from TechCon 03:50 Patrick's views of TechCon from a server,
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.
Alation
OCTOBER 16, 2019
Whether we’re speaking to data analysts or CDOs, data people almost instantly understand the value of the Alation Data Catalog. Faces light up when we describe how Alation helps enterprises find, understand, trust, use and reuse data. The response is usually some form of, “exactly, that’s the problem my company needs to solve!” At some […]. The post Forrester Does the Math on the ROI of the Alation Data Catalog appeared first on Alation.
Twilio Segment
OCTOBER 17, 2019
How we managed to reduce our infrastructure cost by 30%. And how you can too.
KDnuggets
OCTOBER 18, 2019
If you want to launch your data science skills into freelance work, then check out these important tips to help you kick start your next adventure in data.
DataRobot
OCTOBER 18, 2019
There have been anecdotal complaints that the postseason baseball is deader than its regular-season counterpart, so we used DataRobot to analyze 100k+ batted balls in 2019.
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.
Dataconomy
OCTOBER 16, 2019
If you’re a Data Scientist, you’ve likely spent months earnestly developing and then deploying a single predictive model. The truth is that once your model is built – that’s only half the battle won. A quarter of a Data Scientist’s working life often goes something like this: You met with. The post A Data Scientist’s relationship with building Predictive Models appeared first on Dataconomy.
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.
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
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Start-ups & SMBs launching products quickly must bundle dashboards, reports, & self-service analytics into apps. Customers expect rapid value from your product (time-to-value), data security, and access to advanced capabilities. Traditional Business Intelligence (BI) tools can provide valuable data analysis capabilities, but they have a barrier to entry that can stop small and midsize businesses from capitalizing on them.
KDnuggets
OCTOBER 16, 2019
This post is about various evaluation metrics and how and when to use them.
KDnuggets
OCTOBER 18, 2019
Read this quick overview of neural networks and learn how to implement your first in very few lines using Keras.
KDnuggets
OCTOBER 14, 2019
Density estimation is estimating the probability density function of the population from the sample. This post examines and compares a number of approaches to density estimation.
KDnuggets
OCTOBER 15, 2019
In this research guide, we’ll look at deep learning papers aimed at synthesizing video frames within an existing video.
Speaker: Kevin Kai Wong, President of Emergent Energy Solutions
In today's industrial landscape, the pursuit of sustainable energy optimization and decarbonization has become paramount. Manufacturing corporations across the U.S. are facing the urgent need to align with decarbonization goals while enhancing efficiency and productivity. Unfortunately, the lack of comprehensive energy data poses a significant challenge for manufacturing managers striving to meet their targets.
KDnuggets
OCTOBER 15, 2019
The way we control our data isn’t working. Data is as vulnerable as ever. Download this white paper, which outlines lessons about how data science and governance programs can, if implemented properly, reinforce each other’s objective.
KDnuggets
OCTOBER 18, 2019
In this second part we want to outline our own experience building an AI application and reflect on why we chose not to utilise deep learning as the core technology used.
KDnuggets
OCTOBER 17, 2019
While effective anonymization technology remains elusive, understanding the history of this challenge can guide data science practitioners to address these important concerns through ethical and responsible use of sensitive information.
KDnuggets
OCTOBER 16, 2019
This week on KDnuggets: Beyond Word Embedding: Key Ideas in Document Embedding; The problem with metrics is a big problem for AI; Activation maps for deep learning models in a few lines of code; There is No Such Thing as a Free Lunch; 8 Paths to Getting a Machine Learning Job Interview; and much, much more.
Speaker: Anne Steiner and David Laribee
As a concept, Developer Experience (DX) has gained significant attention in the tech industry. It emphasizes engineers’ efficiency and satisfaction during the product development process. As product managers, we need to understand how a good DX can contribute not only to the well-being of our development teams but also to the broader objectives of product success and customer satisfaction.
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