2017 Aug Opinions, Interviews
All (112) | Courses, Education (8) | Meetings (15) | News, Features (17) | Opinions, Interviews (28) | Software (2) | Tutorials, Overviews (35) | Webcasts & Webinars (7)
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Top 10 Machine Learning Use Cases: Part 1 - Aug 31, 2017.
This post is the first in a series whose aim is to shake up our intuitions about what machine learning is making possible in specific sectors — to look beyond the set of use cases that always come to mind. - Connecting the dots for a Deep Learning App - Aug 31, 2017.
We show how to build a Deep Learning app which does sentiment analysis on movie reviews. Try it yourself!
- Are physicians worried about computers machine learning their jobs? - Aug 30, 2017.
We review JAMA article on “Unintended Consequences of Machine Learning in Medicine” and argue that a number of alarming opinions in this pieces are not supported by evidence.
- Vital Statistics You Never Learned… Because They’re Never Taught - Aug 29, 2017.
Marketing scientist Kevin Gray asks Professor Frank Harrell about some important things we often get wrong about statistics.
- An Intuitive Guide to Deep Network Architectures - Aug 28, 2017.
How and why do different Deep Learning models work? We provide an intuitive explanation for 3 very popular DL models: Resnet, Inception, and Xception.
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Deep Learning is not the AI future - Aug 25, 2017.
While Deep Learning had many impressive successes, it is only a small part of Machine Learning, which is a small part of AI. We argue that future AI should explore other ways beyond DL. - Wrapping Our Primate Brains Around AI’s Next Grand Challenge - Aug 24, 2017.
AI has moved beyond the Turing Test and is literally “moving” towards new directions. We argue that the new AI grand challenge is to allow intelligence to become more embodied and animal-like.
- Understanding overfitting: an inaccurate meme in Machine Learning - Aug 23, 2017.
Applying cross-validation prevents overfitting is a popular meme, but is not actually true – it more of an urban legend. We examine what is true and how overfitting is different from overtraining.
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Machine Learning vs. Statistics: The Texas Death Match of Data Science - Aug 23, 2017.
Throughout its history, Machine Learning (ML) has coexisted with Statistics uneasily, like an ex-boyfriend accidentally seated with the groom’s family at a wedding reception: both uncertain where to lead the conversation, but painfully aware of the potential for awkwardness. - Data Podcast: Gregory Piatetsky-Shapiro, KDnuggets President, a top Big Data Influencer - Aug 22, 2017.
An episode of Data Podcast, featuring Gregory Piatetsky-Shapiro, discussing KDnuggets, trends in Big Data and Machine Learning, Automation of Data Science, Bias in Algorithms and AI, and more.
- 37 Reasons why your Neural Network is not working - Aug 22, 2017.
Over the course of many debugging sessions, I’ve compiled my experience along with the best ideas around in this handy list. I hope they would be useful to you.
- O’Reilly NYC AI Conference Highlights: Explainable AI, Vector Representation, Bias, and Future - Aug 21, 2017.
The answer to questions of trust and bias in AI is largely seen in the focus on Explainable AI. Although traditionally viewed as "black boxes", AI and machine learning systems are not ontologically inscrutable.
- Using AI to Super Compress Images - Aug 21, 2017.
Neural Network algorithms are showing promising results for different complex problems. Here we discuss how these algorithms are used in image compression.
- What is the most important step in a machine learning project? - Aug 18, 2017.
In any machine learning project, business understanding is very important. But in practice, it does not get enough attention. Here we explain what questions should be asked.
- Causation: The Why Beneath The What - Aug 18, 2017.
A lot of marketing research is aimed at uncovering why consumers do what they do and not just predicting what they'll do next. Marketing scientist Kevin Gray asks Harvard Professor Tyler VanderWeele about causal analysis, arguably the next frontier in analytics.
- The Rise of GPU Databases - Aug 17, 2017.
The recent but noticeable shift from CPUs to GPUs is mainly due to the unique benefits they bring to sectors like AdTech, finance, telco, retail, or security/IT . We examine where GPU databases shine.
- A New Beginning to Deep Learning - Aug 17, 2017.
I won't give you the clichéd line that it's never too late because that's not the point. It is actually because, a term that I loved as soon as I came across it- 'The AI Winter' - doesn't seem to ever be going to return again.
- Lessons Learned From Benchmarking Fast Machine Learning Algorithms - Aug 16, 2017.
Boosted decision trees are responsible for more than half of the winning solutions in machine learning challenges hosted at Kaggle, and require minimal tuning. We evaluate two popular tree boosting software packages: XGBoost and LightGBM and draw 4 important lessons.
- DeepMind Relational Reasoning Networks Demystified - Aug 15, 2017.
Every time DeepMind publishes a new paper, there is frenzied media coverage around it. We examine what is and is not real in recent work described as “DeepMind Neural Network Can Make Sense of Objects Around It”.
- 4 Industries Being Transformed by Machine Learning and Robotics - Aug 15, 2017.
When used in combination with big data and machine learning, both AI and robotics can actively improve over time as they collect more information. You don’t have to look far to see how these technologies have revolutionized the world, and continue to do so.
- Data Version Control in Analytics DevOps Paradigm - Aug 14, 2017.
DevOps and DVC tools can help reduce time data scientists spend on mundane data preparation and achieve their dream of focusing on cool machine learning algorithms and interesting data analysis.
- What Artificial Intelligence and Machine Learning Can Do—And What It Can’t - Aug 10, 2017.
I have seen situations where AI (or at least machine learning) had an incredible impact on a business—I also have seen situations where this was not the case. So, what was the difference?
- Sampling: A Primer - Aug 8, 2017.
Though it doesn’t get a lot of buzz, sampling is fundamental to any field of science. Marketing scientist Kevin Gray asks Dr. Stas Kolenikov, Senior Scientist at Abt Associates, what marketing researchers and data scientists most need to know about it.
- Why Apache Arrow is the future for open source-columnar memory analytics - Aug 7, 2017.
Apache Arrow is a de-facto standard for columnar in-memory analytics. In the coming years we can expect all the big data platforms adopting Apache Arrow as its columnar in-memory layer.
- Top 3 Breakthroughs in Combating Financial Crime - Aug 3, 2017.
AI and Analytics driven solutions have been widely adopted across different industries for various purposes. However, only a handful of banks around the world are working with advanced analytics and artificial intelligence technologies to improve their risk and compliance activities.
- What is hardcore data science – in practice? - Aug 1, 2017.
Data Science expert Mikio Braun on the anatomy of an architecture to bring data science into production. Learn more at his talk at Strata NYC - Use code KDNU for additional 20% off (best price ends Aug 11).
- How will Big Data companies monetize data in 2018? - Aug 1, 2017.
In today’s data driven economy, Data is a strategic asset to a company and data monetization is prime focus of many companies. Let’s see how data monetization will be achieved in 2018.
- Beautiful Python Visualizations: An Interview with Bryan Van de Ven, Bokeh Core Developer - Aug 1, 2017.
Read this insightful interview with Bokeh's core developer, Bryan Van de Ven, and gain an understanding of what Bokeh is, when and why you should use it, and what makes Bryan a great fit for helming this project.
Top 10 Machine Learning Use Cases: Part 1
Deep Learning is not the AI future
Machine Learning vs. Statistics: The Texas Death Match of Data Science
The Rise of GPU Databases
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