2019 May Opinions
All (82) | Courses, Education (2) | Meetings (3) | News (5) | Opinions (30) | Top Stories, Tweets (10) | Tutorials, Overviews (30) | Webcasts & Webinars (2)
- A Step-by-Step Guide to Transitioning your Career to Data Science – Part 1 - May 31, 2019.
If you are looking to transition your career to data science, don't immediately start learning Python or R. Instead, leverage the domain expertise you have accumulated over the years. Here's a foolproof guide on how to do that.
- What Does a Lady Tasting Tea Have to Do with Science? - May 31, 2019.
Design of Experiments (DOE) is a statistical concept used to find the cause-and-effect relationships. Surprisingly, an experiment arising from a casual conversation about tea-drinking is one of the first examples of an experiment designed using statistical ideas.
- Python leads the 11 top Data Science, Machine Learning platforms: Trends and Analysis - May 30, 2019.
Python continues to lead the top Data Science platforms, but R and RapidMiner hold their share; Almost 50% have used Deep Learning tools; SQL is steady; Consolidation continues.
- How the Lottery Ticket Hypothesis is Challenging Everything we Knew About Training Neural Networks - May 30, 2019.
The training of machine learning models is often compared to winning the lottery by buying every possible ticket. But if we know how winning the lottery looks like, couldn’t we be smarter about selecting the tickets?
- Why organizations fail in scaling AI and Machine Learning - May 29, 2019.
We explain why AI needs to understand business processes and how the business processes need to be able to change to bring insight from AI into the process.
- AI in the Family: how to teach machine learning to your kids - May 28, 2019.
AI is all the rage with today’s programmers, but what about the next generation? Machine learning can be introduced to young ones just now learning about code, and you can help spark their interest.
- ICLR 2019 highlights: Ian Goodfellow and GANs, Adversarial Examples, Reinforcement Learning, Fairness, Safety, Social Good, and all that jazz - May 27, 2019.
We provide an overview of the main themes and topics discussed at this years International Conference on Learning Representations (ICLR).
- Fixing a Major Weakness in Machine Learning of Images with Hinton’s Capsule Networks - May 22, 2019.
We explore Geoffrey Hinton's capsule networks to deal with rotational variance in images.
- 6 Industries Warming up to Predictive Analytics and Forecasting - May 22, 2019.
Here are six sectors that are realizing how beneficial predictive analytics could be, embracing the possibilities of valuable insights extracted from such technology.
- How do you teach physics to machine learning models? - May 21, 2019.
How to integrate physics-based models (these are math-based methods that explain the world around us) into machine learning models to reduce its computational complexity.
- The Data Fabric for Machine Learning – Part 1 - May 21, 2019.
How the new advances in semantics and the data fabric can help us be better at Machine Learning
- Think Like an Amateur, Do As an Expert: Lessons from a Career in Computer Vision - May 17, 2019.
Dr. Takeo Kanade shared his life lessons from an illustrious 50-year career in Computer Vision at last year's Embedded Vision Summit. You have a chance to attend the 2019 Embedded Vision Summit, from May 20-23, in the Santa Clara Convention Center, Santa Clara CA.
- Why Data Professionals Should Negotiate Every Job Offer - May 16, 2019.
Here are six reasons why you shouldn't feel tempted to jump at the chance and take that job offer as it is without first negotiating.
- Machine Learning in Agriculture: Applications and Techniques - May 14, 2019.
Machine Learning has emerged together with big data technologies and high-performance computing to create new opportunities to unravel, quantify, and understand data intensive processes in agricultural operational environments.
- What’s Going to Happen this Year in the Data World - May 14, 2019.
"If we wish to foresee the future of mathematics, our proper course is to study the history and present condition of the science." Henri Poncairé.
- What my first Silver Medal taught me about Text Classification and Kaggle in general? - May 13, 2019.
A first-hand account of ideas tried by a competitor at the recent kaggle competition 'Quora Insincere questions classification', with a brief summary of some of the other winning solutions.
- Modeling 101 - May 13, 2019.
In the past couple of decades, innovation in statistics and machine learning has been increasing at a rapid pace and we are now able to do things unimaginable when I began my career.
- Data Science Poem - May 11, 2019.
A poem about Data Science.
- How (not) to use Machine Learning for time series forecasting: Avoiding the pitfalls - May 10, 2019.
We outline some of the common pitfalls of machine learning for time series forecasting, with a look at time delayed predictions, autocorrelations, stationarity, accuracy metrics, and more.
- Ethical AI: EU’s New Guidelines and the Future of AI Trustworthiness - May 10, 2019.
The EU has issued a set of guidelines, "Ethics Guidelines for Trustworthy AI" to help tech companies steer towards ethical and inclusive AI as we come to terms with the potential of this technology.
- 5 Things to Review Before Accepting That Data Scientist Job Offer - May 10, 2019.
Before you get too excited and sign the papers for that new data scientist job, and solidify your role as a new hire, make sure you look over these 5 things first.
- “Please, explain.” Interpretability of machine learning models - May 9, 2019.
Unveiling secrets of black box models is no longer a novelty but a new business requirement and we explain why using several different use cases.
- Data Scientist – Best Job of the Year in USA - May 8, 2019.
CareerCast ranks Data Scientist as the top job in USA, with very good work environment, low stress, high growth, and median salary of $114,520.
- 2019 KDnuggets Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months? - May 7, 2019.
Vote in KDnuggets 20th Annual Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months? We will publish the anon data, results, and trends here.
- Data Science vs. Decision Science - May 7, 2019.
Data science and decision science are related but still separate fields, so at some points, it might be hard to compare them directly. We attempted to show our vision of the commonalities, differences, and specific features of data science and decision science.
- The Third Wave Data Scientist - May 6, 2019.
An extensive look at what skills are needed to make up the portfolio of the third wave of data scientists.
- The 3 Biggest Mistakes on Learning Data Science - May 6, 2019.
Data science or whatever you want to call it is not just knowing some programming languages, math, statistics and have “domain knowledge” and here I show you why.
- Strata SF day 2 Highlights: AI and Politics, Chatbots Insights, Forecasting Uncertainty, Scalable Video Analysis, and more - May 3, 2019.
AI influencing Politics, insights from Chatbots, Enterprise Data Cloud, handling Video Big Data, and more takeaways from Strata Data Conference 2019, San Francisco.
- XGBoost Algorithm: Long May She Reign - May 2, 2019.
In recent years, XGBoost algorithm has gained enormous popularity in academic as well as business world. We outline some of the reasons behind this incredible success.
- Which Deep Learning Framework is Growing Fastest? - May 1, 2019.
In September 2018, I compared all the major deep learning frameworks in terms of demand, usage, and popularity. TensorFlow was the champion of deep learning frameworks and PyTorch was the youngest framework. How has the landscape changed?