Sep
2018 Oct Opinions
All (116) | Courses, Education (10) | Meetings (9) | News, Features (16) | Opinions (30) | Top Stories, Tweets (11) | Tutorials, Overviews (32) | Webcasts & Webinars (8)
- Labeling Unstructured Text for Meaning to Achieve Predictive Lift - Oct 31, 2018.
In this post, we examine several advance NLP techniques, including: labeling nouns and noun phrases for meaning, labeling (most often) adverbs and adjectives for sentiment, and labeling verbs for intent.
- Cartoon: Halloween Costume for Big Data. - Oct 31, 2018.
We revisit KDnuggets cartoon looking at the appropriate Halloween costume for Big Data and its companion, No Privacy.
- New Poll: How Important is Understanding Machine Learning Models? - Oct 30, 2018.
New KDnuggets poll is asking: When building Machine Learning / Data Science models in 2018, how often was it important that the model be humanly understandable/explainable? Please vote
- Key Takeaways from AI Conference SF, Day 2: AI and Security, Adversarial Examples, Innovation - Oct 30, 2018.
Highlights and key takeaways from selected keynote sessions on day 2 of AI Conference San Francisco 2018.
- Stop Installing Tensorflow Using pip for Performance Sake! - Oct 30, 2018.
If you aren’t already using conda, I recommend that you start as it makes managing your data science tools much more enjoyable.
- Amazing consistency: Largest Dataset Analyzed / Data Mined – Poll Results and Trends - Oct 29, 2018.
The poll results show amazing consistency to past years, with median answers still in 10-100 gigabytes range. Really Big Data Scientists (100 Petabytes and more) continue to stand apart, but remain small segment where Asian data scientists lead for the first time in this poll.
- Top Obstacles to Overcome when Implementing Predictive Maintenance - Oct 29, 2018.
We investigate how to create a systematic approach to predictive maintenance, ensuring there's enough data to create accurate systems. This post also explains how to identify a failure source and knowing how to predict it.
- Key Takeaways from AI Conference SF, Day 1: Domain Specific Architectures, Emerging China, AI Risks - Oct 29, 2018.
Highlights and key takeaways include Domain Specific Architectures – the next big thing, Emerging China – evolving from copying ideas to true innovation, and Addressing Risks in AI – Security, Privacy, and Ethics.
- AI Masterpieces: But is it Art? - Oct 27, 2018.
While there’s no doubt that the quality of the results in style transfer is outstanding, many were left with feelings that the technique left little room for the concept of art itself, even calling it “… more of a parlor trick than the next revolution in fine art.”
- How I Learned to Stop Worrying and Love Uncertainty - Oct 24, 2018.
This is a written version of Data Scientist Adolfo Martínez’s talk at Software Guru’s DataDay 2017. There is a link to the original slides (in Spanish) at the top of this post.
- How to Define a Machine Learning Problem Like a Detective - Oct 22, 2018.
The common refrain among machine learning practitioners is that it’s as much an art as a science. True enough, but in this discipline, you can only appreciate the former if you understand the latter.
- Holy Grail of AI for Enterprise — Explainable AI - Oct 19, 2018.
Explainable AI (XAI) is an emerging branch of AI where AI systems are made to explain the reasoning behind every decision made by them. We investigate some of its key benefits and design principles.
- New Jobs Sure to Emerge Alongside Artificial Intelligence - Oct 18, 2018.
There’s a lot of doomsaying about AI pushing humans out of jobs and destroying entire industries. Is it as bad as all that? Maybe not!
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Graphs Are The Next Frontier In Data Science - Oct 18, 2018.
GraphConnect 2018, Neo4j’s bi-annual conference, was held in New York City in mid-September. Read about what happened, and why graphs are the next big thing in data science. - Music for Data Scientists? Music by Data Scientists? …What…?! - Oct 17, 2018.
Introducing Mean Reversion, an NYC-based songwriting duo comprising of data scientist Foster Provost and statistician Cliff Hurvich.
- Select Your Analytics Adventure – Analytics On-boarding - Oct 15, 2018.
Lower the barriers to productivity by employing a “Choose your own adventure” approach to on-boarding your new analytics team members.
- Machine Reading Comprehension: Learning to Ask & Answer - Oct 11, 2018.
Investigating the dual ask-answer network, covering the embedding, encoding, attention and output layer, as well as the loss function, with code examples to help you get started.
- Evaluating the Business Value of Predictive Models in Python and R - Oct 11, 2018.
In these blogs for R and python we explain four valuable evaluation plots to assess the business value of a predictive model. We show how you can easily create these plots and help you to explain your predictive model to non-techies.
- Top 8 Python Machine Learning Libraries - Oct 9, 2018.
Part 1 of a new series investigating the top Python Libraries across Machine Learning, AI, Deep Learning and Data Science.
- How To Learn Data Science If You’re Broke - Oct 9, 2018.
A first-hand account on how to learn data science on a budget, with advice covering useful resources, a recommended curriculum, typical concepts, building a portfolio and more.
- Semantic Interoperability: Are you training your AI by mixing data sources that look the same but aren’t? - Oct 9, 2018.
Semantic interoperability is a challenge in AI systems, especially since data has become increasingly more complex. The other issue is that semantic interoperability may be compromised when people use the same system differently.
- BIG, small or Right Data: Which is the proper focus? - Oct 8, 2018.
For most businesses, having and using big data is either impossible, impractical, costly to justify, or difficult to outsource due to the over demand of qualified resources. So, what are the benefits of using small data?
- Things you should know when traveling via the Big Data Engineering hype-train - Oct 8, 2018.
Maybe you want to join the Big Data world? Or maybe you are already there and want to validate your knowledge? Or maybe you just want to know what Big Data Engineers do and what skills they use? If so, you may find the following article quite useful.
- Basic Image Data Analysis Using Python – Part 4 - Oct 5, 2018.
Accessing the internal component of digital images using Python packages helps the user understand its properties, as well as its nature.
- Why do I Call Myself a Data Scientist? - Oct 5, 2018.
Claimed as the “sexiest job of the 21st Century” here I’ll discuss the reasons for my proclamation as a Data Scientist, beyond the hype.
- 3 Stages of Creating Smart - Oct 4, 2018.
The technology is advancing at a pace that should enable any company to create “smart” products, things and spaces. But how does one go about actually creating smart?
- Top 3 Trends in Deep Learning - Oct 3, 2018.
We investigate the intermediate stage of deep learning, and the trends that are emerging in response to the challenges at this stage, including Interoperability and the multi-deployment options.
- How to Create a Simple Neural Network in Python, by Dr. Michael J. Garbade - Oct 2, 2018.
The best way to understand how neural networks work is to create one yourself. This article will demonstrate how to do just that.
- 5 Reasons Why You Should Use Cross-Validation in Your Data Science Projects - Oct 2, 2018.
In cross-validation, we do more than one split. We can do 3, 5, 10 or any K number of splits. Those splits called Folds, and there are many strategies we can create these folds with.
- A Right to Reasonable Inferences - Oct 1, 2018.
As shown in this paper, individuals are granted little control over how their personal data is used to draw inferences about them. Compared to other types, inferences are effectively ‘economy class’ personal data in the General Data Protection Regulation (GDPR).
Graphs Are The Next Frontier In Data Science
Top 8 Python Machine Learning Libraries