Oct
2018 Nov Opinions
All (105) | Courses, Education (10) | Meetings (9) | News (13) | Opinions (32) | Top Stories, Tweets (9) | Tutorials, Overviews (24) | Webcasts & Webinars (8)
- Interpretability is crucial for trusting AI and machine learning
- Nov 30, 2018.
We explain what exactly interpretability is and why it is so important, focusing on its use for data scientists, end users and regulators.
- Linking Data Science Activities to Business Initiatives Using the Hypothesis Development Canvas
- Nov 29, 2018.
The Hypothesis Development Canvas is an effective and concise tool that integrates the different elements of the “Thinking Like A Data Scientist” process into a single document.
- How to Build a Machine Learning Team When You Are Not Google or Facebook
- Nov 28, 2018.
If you don’t have a clear application for machine learning, you’re going to regret your investment. We provide tips on how to go about setting up your machine learning team - no matter the size of your business.
- How to Engineer Your Way Out of Slow Models
- Nov 27, 2018.
We describe how we handle performance issues with our deep learning models, including how to find subgraphs that take a lot of calculation time and how to extract these into a caching mechanism.
- Bringing Machine Learning Research to Product Commercialization
- Nov 27, 2018.
In this blog post I want to share some of the insights into the differences between academia and industry when applying deep learning to real-world problems as we experienced them at Merantix over the last two years.
- My secret sauce to be in top 2% of a Kaggle competition
- Nov 26, 2018.
A collection of top tips on ways to explore features and build better machine learning models, including feature engineering, identifying noisy features, leakage detection, model monitoring, and more.
- Data Science Strategy Safari: Aligning Data Science Strategy to Org Strategy
- Nov 26, 2018.
The title of this post is derived by drawing inspiration from Mintzberg’s seminal work. In this post, I am attempting to take you on a safari through the data science strategy formulation process.
- Top 5 domains Big Data analytics helps to transform
- Nov 23, 2018.
Big data analytics gives a competitive advantage to companies across many industries, especially, financial services, e-commerce, aviation, transportation, logistics, and energy. It enables to reduce downtime, mitigate risks, cut costs, and improve performance.
- 6 Goals Every Wannabe Data Scientist Should Make for 2019
- Nov 22, 2018.
Looking to embark on a new path as a data scientist? That goal may be worthy, but it's essential for people to also set goals for 2019 that will help them get closer to that broader aim.
- Cartoon: Thanksgiving, Big Data, and Turkey Data Science.
- Nov 22, 2018.
A classic KDnuggets Thanksgiving cartoon examines the predicament of one group of fowl Data Scientists.
- Autonomy – Do we have the choice?
- Nov 21, 2018.
Choice of taking decision or not taking a decision requires a free will. Machines do not have free will. They do what they do, some machines do intelligent things but not with choice. Interesting question to think is - what is choice? or what is autonomy?
- Word Morphing – an original idea
- Nov 20, 2018.
In this post, we describe how to utilise word2vec's embeddings and A* search algorithm to morph between words.
- Machine Learning in Action: Going Beyond Decision Support Data Science
- Nov 20, 2018.
In order to disrupt business, machine learning models must adopt a product-focused approach, which is a much more significant undertaking.
- How Important is that Machine Learning Model be Understandable? We analyze poll results
- Nov 19, 2018.
About 85% of respondents said it was always or frequently important that Machine Learning model be understandable. This was is especially important for academic researchers, and surprisingly more in US/Canada than in Europe or Asia.
- What I Learned About Machine Learning at ODSC West 2018
- Nov 19, 2018.
Reflecting back on the ODSC West 2018 conference, with a review of some of the best talks on topics including active learning, interactive coefficient plots, time-series forecasting, and more.
- Predictive Analytics in 2018: Salaries & Industry Shifts
- Nov 19, 2018.
Highlights from Burtch Works Study: Salaries of Predictive Analytics Professionals include: salaries remain steady, which industries pay the most, and which industries are attracting more analytics professionals.
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The Big Data Game Board™
- Nov 19, 2018.
Move aside “Monopoly,” “Risk,” and “Snail Race!” Time to teach the youth of the world of an important, career-advancing game: how to leverage data and analytics to change your life! Introducing the “Big Data Game Board™”! - Anticipating the next move in data science – my interview with Thomson Reuters
- Nov 17, 2018.
Like chess, Big Data is a combination of science, art and play; Gregory Piatetsky-Shapiro of KDnuggets helps data devotees discover winning moves - my Thomson Reuters interview.
- Using Uncertainty to Interpret your Model
- Nov 16, 2018.
We outline why you should care about uncertainty and discuss the different types, including model, data and measurement uncertainty and what different purposes these all serve.
- Strategy: Customer Analytics: Are you Profiting from your Data?
- Nov 14, 2018.
Introducing Wharton's Customer Analytics program, that helps participants make the connection between the numbers and the narrative, making it easier for them to help others understand the data they are collecting.
- Metadata Enrichment is Essential to Realize the Value of Open Datasets
- Nov 14, 2018.
The last few years have seen great advancement in AI technologies for data science and analytics. With analytics engines capable of ingesting and analyzing almost any amount and type of data, the bottleneck has shifted from the technology to the data itself.
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What is the Best Python IDE for Data Science?
- Nov 14, 2018.
Before you start learning Python, choose the IDE that suits you the best. We examine many available tools, their pros and cons, and suggest how to choose the best Python IDE for you. - The ultimate guide to starting AI
- Nov 13, 2018.
A step-by-step overview of how to begin your project, including advice on how to craft a wise performance metric, setting up testing criteria to overcome human bias, and more.
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To get hired as a data scientist, don’t follow the herd
- Nov 12, 2018.
Key tips, including advice on how to step out of your comfort zone and sometimes overlooked important skills that will impress employers. Check also the audio version with additional advice. - The Long Tail of Medical Data
- Nov 12, 2018.
This article discusses some issues related to medical data, relating specifically to power law distributions and computer aided diagnosis. Read on to see machine learning's place in the puzzle.
- What does a data scientist REALLY look like?
- Nov 9, 2018.
Using the responses from Stack Overflow's 2018 Annual Developer Survey, we attempt to build a portrait of data scientists today, including a look at gender, skills, job satisfaction, and more.
- Best Practices for Using Notebooks for Data Science
- Nov 8, 2018.
Are you interested in implementing notebooks for data science? Check out these 5 things to consider as you begin the process.
- Latest Trends in Computer Vision Technology and Applications
- Nov 7, 2018.
We investigate the advancements in deep learning, the rise of edge computing, object recognition with point cloud, VR and AR enhanced merged reality, semantic instance segmentation and more.
- The Most in Demand Skills for Data Scientists
- Nov 2, 2018.
Data scientists are expected to know a lot — machine learning, computer science, statistics, mathematics, data visualization, communication, and deep learning. How should data scientists who want to be in demand by employers spend their learning budget?
- Data Science “Paint by the Numbers” with the Hypothesis Development Canvas
- Nov 2, 2018.
Now you are ready to take the next step from a Big Data MBA perspective by building off of the Business Model Canvas to flesh out the business use cases – or hypothesis – which is where we can become more effective at leveraging data and analytics to optimize our the business.
- Why AI will not replace radiologists
- Nov 1, 2018.
We investigate some of the reasons why radiologists will be safe from AI, including the fact that humans will always maintain ultimate responsibility, how productivity gains will drive demand, and more.
- How Data Science Is Improving Higher Education
- Nov 1, 2018.
Increasingly, colleges and universities, as well as governments, are using data science to improve the ways educational institutions do everything from recruiting to engaging with students to budgeting.
Bayesian deep learning and near-term quantum computers: A cautionary tale in quantum machine learning
A Summary of DeepMind’s Protein Folding Upset at CASP13