KDnuggets

SEPTEMBER 23, 2019

Our list of deep learning researchers and industry leaders are the people you should follow to stay current with this wildly expanding field in AI. From early practitioners and established academics to entrepreneurs and today’s top corporate influencers, this diverse group of individuals is leading the way into tomorrow’s deep learning landscape.

Deep Learning 301

Deep Learning Deep Learning AI AI 301

Analytics Vidhya

SEPTEMBER 24, 2019

Overview Google’s BERT has transformed the Natural Language Processing (NLP) landscape Learn what BERT is, how it works, the seismic impact it has made, The post Demystifying BERT: A Comprehensive Guide to the Groundbreaking NLP Framework appeared first on Analytics Vidhya.

Natural Language Processing 301

Natural Language Processing Analytics Analytics Python 301

Dataconomy

SEPTEMBER 26, 2019

Before you start using the next amazing new femtech innovation, you may want to weigh the benefits against your right to privacy. You might want to think twice before accepting that sweet employer incentive for participating in health monitoring. Yes. Corporate wellness monitoring programs offer benefits, but they also have. The post The Rise Of “Menstrual Surveillance” and the Fight for Data Privacy in Women’s Health appeared first on Dataconomy.

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Data Science 101

SEPTEMBER 24, 2019

A number of new impactful open source projects have been released lately. Open Source Data Science Projects. Pythia – from Facebook for deep learning with vision and language, “such as answering questions related to visual data and automatically generating image captions “ InterpretML – from Microsoft, ” package for training interpretable models and explaining blackbox systems “ ML framework for Julia – from Alan Turing Institute, MLJ is a machine learni

Data Science 104

Data Science Deep Learning Deep Learning Machine Learning 104

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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?

AI

KDnuggets

SEPTEMBER 24, 2019

Deep Learning is/has become the hottest skill in Data Science at the moment. There is a plethora of articles, courses, technologies, influencers and resources that we can leverage to gain the Deep Learning skills.

Deep Learning 298

Deep Learning Deep Learning Data Science Data Scientist 298

Analytics Vidhya

SEPTEMBER 22, 2019

Overview How do search engines like Google understand our queries and provide relevant results? Learn about the concept of information extraction We will apply. The post How Search Engines like Google Retrieve Results: Introduction to Information Extraction using Python and spaCy appeared first on Analytics Vidhya.

Python 300

Python Analytics Analytics Natural Language Processing 300

Data Science 101

SEPTEMBER 26, 2019

I am putting together some of my own resources on Data Strategy. Here are a few of the top resources I found helpful so far. What is a Data Strategy? – various definitions of a data strategy The 5 essential Components of a Data Strategy – a detailed whitepaper(PDF) from SAS How to Create a Successful Data Strategy – a detailed report from MIT How Do You Develop a Data Strategy (including 6 steps) – by Bernard Marr, He has created more data strategies than anyone, so his a

AI 97

AI AI Data Governance Data Science 97

KDnuggets

SEPTEMBER 23, 2019

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.

Python 281

Python 281

Analytics Vidhya

SEPTEMBER 23, 2019

Introduction TensorFlow is easily the most widely used deep learning framework right now. The idea behind TensorFlow (TF) has even spawned multiple products, such. The post DataHack Radio: All you Need to Know about TensorFlow with Google’s Paige Bailey appeared first on Analytics Vidhya.

Deep Learning 220

Deep Learning Deep Learning Analytics Analytics 220

Smart Data Collective

SEPTEMBER 25, 2019

Data breaches are becoming more common in today’s society. Hackers know they can sell compromised information on the dark web or use it for purposes such as blackmail. However, encryption technology for data protection is widely available. It involves protecting information with cryptography via a scrambled code. Only people with the key to decode the data can read it.

Data Quality 84

Data Quality 84

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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.

Database

DataCentric podcast

SEPTEMBER 25, 2019

A catch-up podcast as Moor Insights & Strategy technology analysts Matt Kimball & Steve McDowell run through a very news-filled September. The guys recap Red Hat Summit, Pure Accelerate, and also hit product announcements from Dell EMC, IBM, and Huawei, bemoan the dwindling quality of conference SWAG, and more. Much, much, more: Red Hat might be owned by IBM, but its about helping enterprises manage digital transformation, focused on where "opportunity and innovation intersect" S

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KDnuggets

SEPTEMBER 24, 2019

How can you keep your machine learning models and data organized so you can collaborate effectively? Discover this new tool set available for better version control designed for the data scientist workflow.

Data Scientist 260

Data Scientist Machine Learning Machine Learning 260

Analytics Vidhya

SEPTEMBER 25, 2019

Do we need to change the way we learn? Before you read any further, here is a sneak peak of our new experiential learning. The post Introducing “PocketML” – an Experiential Learning Platform for Data Science appeared first on Analytics Vidhya.

Data Science 220

Data Science Analytics Analytics Python 220

Smart Data Collective

SEPTEMBER 26, 2019

Traditionally, the world of investing was bland and exclusive. Investment vehicles were not very different from one another and minimum capital requirements meant that even this was reserved for the few who had the means. Most ordinary people had to settle for a savings account at their local bank while some even opted to simply put their savings under their mattress.

Big Data 81

Big Data Big Data Data Mining Data Mining 81

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.

DataRobot

SEPTEMBER 25, 2019

Recently, more than 200 delegates joined a DataRobot team of local and international staff in Sydney, Australia for the AI Experience Roadshow. As the GM for Asia Pacific, I kicked off the event by stating that the AI and machine learning revolution is transforming global and local economies, markets, and companies. As a disruptive technology, AI and machine learning can be difficult, though worthwhile.

Machine Learning 13

Machine Learning Machine Learning AI AI 13

KDnuggets

SEPTEMBER 26, 2019

Learn about the the current and future issues of data science and possible solutions from this interview with IADSS Co-founder, Dr. Usama Fayyad following his keynote speech at ODSC Boston 2019.

Data Science 253

Data Science Analytics Analytics 253

KDnuggets

SEPTEMBER 25, 2019

As a data scientist, you can get lost in your daily dives into the data. Consider this advice to be certain to follow in your work for being diligent and more impactful for your organization.

Data Scientist 248

Data Scientist Clean Data 248

KDnuggets

SEPTEMBER 26, 2019

This article provides a brief introduction to working with natural language (sometimes called “text analytics”) in Python using spaCy and related libraries.

Python 258

Python Analytics Analytics 258

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.

Data Science

KDnuggets

SEPTEMBER 26, 2019

This article shows you how to separate your customers into distinct groups based on their purchase behavior. For the R enthusiasts out there, I demonstrated what you can do with r/stats, ggradar, ggplot2, animation, and factoextra.

Analytics 232

Analytics Analytics 232

KDnuggets

SEPTEMBER 24, 2019

In this article, we’ll look at a couple of papers aimed at solving the problem of automated speech recognition with machine and deep learning.

Deep Learning 256

Deep Learning Deep Learning 256

KDnuggets

SEPTEMBER 26, 2019

Join the Crunch Data Conference in Budapest, Oct 16-18, with stellar speakers from companies like Facebook, Netflix and LinkedIn. Use the discount code ‘KDNuggets’ to save $100 off your conference ticket.

Data Analyst 223

Data Analyst Analytics Analytics Data Science 223

KDnuggets

SEPTEMBER 27, 2019

Take me out to the ballgame! Take me out to the crowd! For the 2,829 seasons that have been played for 101 baseball teams since 1880, which seasons were unlike any others? Using SAX Encoding to recognize patterns in time series data, the most special years in baseball can be found.

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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.

Analytics

KDnuggets

SEPTEMBER 25, 2019

Today, as companies have finally come to understand the value that data science can bring, more and more emphasis is being placed on the implementation of data science in production systems. And as these implementations have required models that can perform on larger and larger datasets in real-time, an awful lot of data science problems have become engineering problems.

Data Science 215

Data Science Data Engineering Data Engineer Data Engineering 215

KDnuggets

SEPTEMBER 27, 2019

The article contains a brief introduction to various concepts related to Hierarchical clustering algorithm.

Clustering 273

Clustering Algorithm Python Machine Learning 273

KDnuggets

SEPTEMBER 27, 2019

This live webinar, Oct 2 2019, will instruct data scientists and machine learning engineers how to build manage and deploy auto-adaptive machine learning models in production. Save your spot now.

Machine Learning 203

Machine Learning Machine Learning Data Scientist 203

KDnuggets

SEPTEMBER 23, 2019

Register now for this webinar, Sep 25 @ 12 PM ET, for a clear approach on how to apply machine learning language technology to massive, unstructured data sets in order to create predictive models of what may be the next “it” ingredient, color, flavor or pack size.

Machine Learning 202

Machine Learning Machine Learning Analytics Analytics 202

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.

Analytics

KDnuggets

SEPTEMBER 23, 2019

Also: Explore the world of Bioinformatics with Machine Learning; My journey path from a Software Engineer to BI Specialist to a Data Scientist; 5 Beginner Friendly Steps to Learn Machine Learning and Data Science with Python; 10 Great Python Resources for Aspiring Data Scientists.

Data Science 197

Data Science Data Scientist Machine Learning Machine Learning 197

KDnuggets

SEPTEMBER 25, 2019

Penn State’s fully online data analytics program uniquely prepares students to advance their career in data science. Penn State offers 3 intakes every year and reviews applications on a rolling basis. GMAT or GRE waivers are available to highly qualified candidates. Learn more now.

Data Science 192

Data Science Analytics Analytics 192

KDnuggets

SEPTEMBER 25, 2019

This article covers the beta distribution, and explains it using baseball batting averages.

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KDnuggets

SEPTEMBER 24, 2019

Of all data quality characteristics, we consider consistency and accuracy to be the most difficult ones to measure. Here, we describe the challenges that you may encounter and the ways to overcome them.

Data Quality 171

Data Quality 171

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.