Dataconomy

APRIL 26, 2017

According to the prediction of IDC Futurescapes, Two-thirds of Global 2000 Enterprises CEOs will center their corporate strategy on digital transformation. A major part of the strategy should include machine-learning (ML) solutions. The implementation of these solutions could change how these enterprises view customer value and internal operating model today.

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Distill

APRIL 4, 2017

We often think of optimization with momentum as a ball rolling down a hill. This isn't wrong, but there is much more to the story.

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Explosion

APRIL 1, 2017

Short of Artificial General Intelligence, we'll always need some way of specifying what we're trying to compute. Labelled examples are a great way to do that, but the process is often tedious. However, the dissatisfaction with supervised learning is misplaced. Instead of waiting for the unsupervised messiah to arrive, we need to fix the way we're collecting and reusing human knowledge.

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Supervised Learning 40

Christian Haschek

APRIL 23, 2017

I wanted to do this project for years but only recently decided to give it a try.

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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

Dataconomy

APRIL 21, 2017

It is no doubt that the sub-field of machine learning / artificial intelligence has increasingly gained more popularity in the past couple of years. As Big Data is the hottest trend in the tech industry at the moment, machine learning is incredibly powerful to make predictions or calculated suggestions based. The post Intro to Machine Learning: 10 Essential Algorithms For Machine Learning Engineers appeared first on Dataconomy.

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Dataconomy

APRIL 24, 2017

Many process manufacturing owner-operators in this next phase of a digital shift have engaged in technology pilots to explore options for reducing costs, meeting regulatory compliance, and/or increasing overall equipment effectiveness (OEE). Despite this transformation, the adoption of advanced analytics tools still presents certain challenges. The extensive and complicated tooling.

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Analytics Analytics Data Science 195

Dataconomy

APRIL 10, 2017

Business leaders are growing weary of making further investments in business intelligence (BI) and big data analytics. Beyond the challenging technical components of data-driven projects, BI and analytics services have yet to live up to the hype. Early adopters and proponents were quick to frame solutions as miraculous reservoirs of. The post The Data Pipeline – Analytics at the Speed of Business appeared first on Dataconomy.

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Dataconomy

APRIL 28, 2017

As your business succeeds, there will come a point when you have to expand your market. A research by Accenture predicts that B2C ecommerce will reach $3.4 trillion globally as more people around the globe prefer purchasing online. Forrester also expects more B2B purchases to shift online as well. Because. The post Payments – How Fintech Can Fuel Global Expansion appeared first on Dataconomy.

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Dataconomy

APRIL 19, 2017

Deep neural networks and Deep Learning are powerful and popular algorithms. And a lot of their success lays in the careful design of the neural network architecture. I wanted to revisit the history of neural network design in the last few years and in the context of Deep Learning. For. The post The History of Neural Networks appeared first on Dataconomy.

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Deep Learning Deep Learning Algorithm Data Science 183

Dataconomy

APRIL 25, 2017

In May of 2012, just 4 weeks before the official date, the opening of Berlin’s new international airport is announced to be delayed for another couple of weeks. Weeks became months and months became years. The latest prediction for its actual opening is late 2018. There is a huge mismatch. The post Before Artificial Intelligence takes over our jobs, it will organize our work. appeared first on Dataconomy.

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Artificial Intelligence Artificial Intelligence Machine Learning Machine Learning 172

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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

Dataconomy

APRIL 5, 2017

Financial markets offer countless ways of making (or losing) money. A key distinction among them is the investment horizon, which can range from fractions of a second to years. Walnut Algorithms and Global Systematic Investors are new investment management firms representing the high-frequency and low-frequency sides, respectively. I sat down. The post Investing, fast and slow – Part 1: The Present and the Future of AI in Investment appeared first on Dataconomy.

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Algorithm AI AI Artificial Intelligence 172

Dataconomy

APRIL 6, 2017

Big data is big news at the moment. The latest Yahoo! data breach, which affected data from over 500 million customers, continues to be discussed by press and public alike, while the role of big data in predicting and even influencing last year’s US election brought the term firmly into. The post Big Data Symbiosis: Using Lessons from Big Data to Protect Big Data appeared first on Dataconomy.

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Big Data Big Data 150

Dataconomy

APRIL 3, 2017

As the collectors of actionable input information, networked smart devices with embedded sensors, software and electronics are a key driving force behind the Internet of Things (IoT). However, they do not generate value for organizations on their own. Powerful, fast database technologies are required to create meaningful insight from the. The post Three Key Facts About Sensors That Are Driving IoT Forward appeared first on Dataconomy.

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Dataconomy

APRIL 14, 2017

The latest version of MySQL is one of the world’s most popular databases. It is open source, reliable, compatible with all major hosting providers, cost-effective, and easy to manage. Many organizations are leveraging the data security and strong transactional support offered by MySQL to secure online transactions and enhance customer. The post The 5 Best Reasons to Choose MySQL – and its 5 Biggest Challenges appeared first on Dataconomy.

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Database Big Data Big Data 139

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.

Dataconomy

APRIL 12, 2017

Financial markets offer countless ways of making (or losing) money. A key distinction among them is the investment horizon, which can range from fractions of a second to years. Walnut Algorithms and Global Systematic Investors are new investment management firms representing the high-frequency and low-frequency sides, respectively. I sat down. The post Investing, fast and slow – Part 2: Investment for Data Scientists 101 appeared first on Dataconomy.

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Dataconomy

APRIL 17, 2017

Oftentimes while analyzing big data we have a need to make checks on pieces of data like number of items in the dataset, number of unique items, and their occurrence frequency. Hash tables or Hash sets are usually employed for this purpose. But when the dataset becomes so enormous that. The post Big Data 101: Intro To Probabilistic Data Structures appeared first on Dataconomy.

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Dataconomy

APRIL 7, 2017

For those of you similarly interested (obsessed?) with the changing role of government statistics relative to the explosion of highly dimensional private sector data, I recommend having a look at Innovations in Federal Statistics: Combining Data Sources While Protecting Privacy from the National Academy of Sciences. It’s an easy read and offers a solid.

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Data Science 132

Christian Haschek

APRIL 17, 2017

[update] I have extended the bot to be able to trade *

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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

Explosion

APRIL 1, 2017

We wrote this post while working on Prodigy , our new annotation tool for radically efficient machine teaching. Prodigy features many of the ideas and solutions for data collection and supervised learning outlined in this blog post. It’s a cloud-free, downloadable tool and comes with powerful active learning models. For more details, see the website or try the live demo.

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