Analytics Vidhya
JUNE 30, 2019
Overview Check out the top 6 machine learning GitHub repositories created in June There’s a heavy focus on NLP again, with XLNet outperforming Google’s. The post 6 Powerful Open Source Machine Learning GitHub Repositories for Data Scientists appeared first on Analytics Vidhya.
Dataconomy
JUNE 28, 2019
Fintech is opening floodgates of opportunity for ambitious startups that previously had no hopes of overcoming barriers to entry in the finance field. With the desire to innovate and succeed, however, gutsy startups are now promoting financial literacy and reaching previously underserved groups with brand-new retail banking and investment services. The post Here is a look at where Fintech is leading us and Why appeared first on Dataconomy.
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Machine Learning (Theory)
JUNE 7, 2019
The first is Sunday afternon during the Industry Expo day. This one is meant to be quite practical, starting with an overview of Contextual Bandits and leading into how to apply the new Personalizer service, the first service in the world functionally supporting general contextual bandit learning. The second is Friday morning. This one is more academic with many topics.
Smart Data Collective
JUNE 23, 2019
In today’s digital age, big data is incorporated into many aspects our daily lives. Big data is essentially massive amounts of data that is used in order to drive strategic decisions. An example of how it is used in daily life is through using online maps such as Google Maps to take the quickest route to work possible. Through the collection of data , patterns of traffic congestion are produced to give you the best route.
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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?
DataRobot
JUNE 28, 2019
It’s been a fun year in the world of professional tennis. From the men’s tour, we’ve witnessed Novak Djokovik winning in Australia, Rafael Nadal winning in France, and Roger Federer rounding out the continued domination of the Big Three. With the emergence of a new generation on the women’s tour, it’s been exciting to watch Naomi Osaka win in Australia and Ashleigh Barty win in France.
Data Science 101
JUNE 3, 2019
Looking for a few academic data science papers to study? Here are a few I have found interesting. The are not all from the past 12 months, but I am including them anyhow. Cloud Programming Simplified: A Berkeley View on Serverless Computing (2019) – Serverless computing is very popular nowadays and this article covers some of the limitations. Playing Atari with Deep Reinforcement Learning (2013) – A bit older, but a classic in the reinforcement learning literature Model Evaluation, M
Data Science Current brings together the best content for data science professionals from the widest variety of thought leaders.
Dataconomy
JUNE 28, 2019
What is the future of AI in Europe and what does it take to build an AI solution that is attractive to investors and customers at the same time? How do we reimagine the battle of “AI vs Human Creativity” in Europe? Is there any company that is not using. The post Where does Europe stand in the development of AI? appeared first on Dataconomy.
Victor Zhou
JUNE 10, 2019
Javascript is dynamically typed : it performs type checking at runtime. On the other hand, a statically typed language like C performs type checking at compile time. Allow me to illustrate the difference. Here’s some simple C code: # include <stdio.h> // Adds one to an integer. int addOne ( int x ) { return x + 1 ; } int main ( ) { const char * value = "2" ; printf ( "%d" , addOne ( value ) ) ; // ?
Smart Data Collective
JUNE 10, 2019
Sustainability is a major concern for many businesses. You probably wouldn’t think that data analytics would be the core solution. Many people believe that the fields of big data and green business have little overlap. However, big data could actually be a wonderful solution for many sustainability problems. People that know me are aware that I have a blog on sustainability, as well as Smart Data Collective.
DataRobot
JUNE 25, 2019
Making predictions has been a part of the banking industry since the world was flat. These days, you would be hard-pressed to identify a line of business or function in a bank that doesn’t have multiple needs for predictive analytics.
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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.
Data Science 101
JUNE 10, 2019
Getting your first data science job might be challenging, but it’s possible to achieve this goal with the right resources. Before jumping into a data science career , there are a few questions you should be able to answer: How do you break into the profession? What skills do you need to become a data scientist? Where are the best data science jobs? First, it’s important to understand what data science is.
Analytics Vidhya
JUNE 12, 2019
Overview Bayes’ Theorem is one of the most powerful concepts in statistics – a must-know for data science professionals Get acquainted with Bayes’ Theorem, The post An Introduction to the Powerful Bayes’ Theorem for Data Science Professionals appeared first on Analytics Vidhya.
Dataconomy
JUNE 13, 2019
As a relatively new role, “data guru” is a challenging job specification to draft for. Organisations are seeking highly-skilled and well-educated individuals to fulfil the position but, the truth is, the data scientist an organisation needs is not a guru, but a colleague. Most organisations forget that recruiting the right. The post How to attract and retain the important, but elusive, data scientist appeared first on Dataconomy.
Victor Zhou
JUNE 10, 2019
Javascript is dynamically typed , but tools like Flow allow you to add static type checking to improve the safety of your codebase. No clue what “typing” is, or never heard of Flow? Read my primer on using Flow to static type check your Javascript. While Flow is most commonly used to add types to client-side Javascript, it can also be easily used with Node.js!
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.
Smart Data Collective
JUNE 1, 2019
. Many bloggers get very frustrated after they have been working for a couple of months. They initially are excited about the possibility of making a six-figure stream of passive income. After they get started though, they discovered that the legwork can be overwhelming. The good news is that machine learning is making it much easier for them to create a successful blogging career, as Jeff Bullas points out.
DataRobot
JUNE 18, 2019
In 2012, DataRobot co-founders Jeremy Achin and Tom de Godoy recognized the profound impact that AI and machine learning could have on organizations, but that there wouldn’t be enough data scientists to meet the demand. The technology they invented, automated machine learning, allowed organizations to scale data science capacity by teaching machines to perform much of the tedious and time-consuming work for a data scientist while also giving them access to hundreds of different algorithms.
Christian Haschek
JUNE 16, 2019
I was looking for a method to keep the plants happy without spending too much money on irrigation
Analytics Vidhya
JUNE 9, 2019
Introduction “I don’t want a full report, just give me a summary of the results” I have often found myself in this situation – The post Comprehensive Guide to Text Summarization using Deep Learning in Python appeared first on Analytics Vidhya.
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.
Dataconomy
JUNE 4, 2019
Here is how CIOs can use AIOps to create a strategy and foundation for the digital future. CIOs and operations teams across the globe are tuned into the rapidly developing area of AIOps (Artificial intelligence for IT operations). As Artificial Intelligence and Machine Learning continue to evolve and advance, IT. The post IT Automation – The Missing Piece in the AIOPS puzzle appeared first on Dataconomy.
Victor Zhou
JUNE 7, 2019
Information Gain, like Gini Impurity , is a metric used to train Decision Trees. Specifically, these metrics measure the quality of a split. For example, say we have the following data: The Dataset What if we made a split at x = 1.5 x = 1.5 x = 1. 5 ? An Imperfect Split This imperfect split breaks our dataset into these branches: Left branch, with 4 blues.
Smart Data Collective
JUNE 14, 2019
Big data is changing our lives in many ways. Some of the ways data impacts us are much more subtle than others. One example is with digital calendars. A number of new data algorithms are being used to make digital calendars more effective. Big data is so important for event planning and preparation. A growing number of companies are using big data to plan their events more effectively.
DataRobot
JUNE 13, 2019
Whether it’s building a winning March Madness bracket or predicting who will win the Stanley Cup , sports analytics is an ever growing field within AI and machine learning. Moneyball was a major tipping point for the industry and the opportunities are continuing to grow. “Now, more than a decade and a half after the events of Moneyball the world of sports has evolved by leaps.
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.
DataCentric podcast
JUNE 24, 2019
During this wide-ranging half-hour discussion recorded live at the 2019 HPE Discover in Las Vegas, hosts Steve McDowell and Matt Kimball talk to HPE's Director of Server Software and Product Security, Bob Moore, about the insidious attack vectors that are emerging. This covers everything from firmware attacks to supply chain interceptions, along with the growth of mafia-like organizations that are driving attacks at an unprecedented scale.
Analytics Vidhya
JUNE 11, 2019
Overview TensorFlow.js (deeplearn.js) enables us to build machine learning and deep learning models right in our browser without needing any complex installation steps There. The post Build a Machine Learning Model in your Browser using TensorFlow.js and Python appeared first on Analytics Vidhya.
Dataconomy
JUNE 6, 2019
Today, customers globally want to buy bespoke goods, made within the time and delivered at the right place. The production of the future will be focused on the personalized market which will respond to the demand changes. How would this tendency affect the future of ERP software? Enterprise resource planning. The post What’s the Future of ERP software in a Fintech Niche?
Depends on the Definition
JUNE 7, 2019
In the previous posts, we saw how to build strong and versatile named entity recognition systems and how to properly evaluate them. But often you want to understand your model beyond the metrics. So in this tutorial I will show you how you can build an explainable and interpretable NER system with keras and the LIME algorithm.
Speaker: Aarushi Kansal, AI Leader & Author and Tony Karrer, Founder & CTO at Aggregage
Software leaders who are building applications based on Large Language Models (LLMs) often find it a challenge to achieve reliability. It’s no surprise given the non-deterministic nature of LLMs. To effectively create reliable LLM-based (often with RAG) applications, extensive testing and evaluation processes are crucial. This often ends up involving meticulous adjustments to prompts.
Smart Data Collective
JUNE 14, 2019
Big data is changing the future of gaming. Dataiku’s Pierre Gutierrez presented a compelling analysis of the role of data analytics in gaming in his piece A Flood Of Gaming Analytics Data With No End In Sight. Some of the ways that data analytics has changed the gaming industry are not noticeable to the average gamer. They don’t directly see the impact, but data is changing the gaming experience in many ways.
DataRobot
JUNE 10, 2019
The main objective for many data scientists is to build machine learning models that predict the outcomes on unseen data that weren’t used in the development process. The performance of a model on unseen data is referred to as its ability to generalize and is ultimately how it will be judged. If generalization does not meet expectations, the result will be poor outcomes and possibly a reduction of stakeholder confidence in machine learning.
DataCentric podcast
JUNE 19, 2019
HPE Discover kicks off with an overwhelming list of themes focused around software-defined, multi-cloud, intelligent edge, and, of course, the stars of the show: Storage and HCI. Hosts Matt Kimball & Steve McDowell provide a high-level recap of all the happenings, and contextualize the announcements. You won't want to miss it! HPE Launches Primera high-end storage.
Analytics Vidhya
JUNE 26, 2019
Overview What does a data scientist do on a day-to-day basis? A popular and must-know question We analyze this question from a data scientist’s. The post Top 5 Must-Read Answers – What does a Data Scientist do on a Daily Basis? appeared first on Analytics Vidhya.
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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.
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