Topics: 
Oct
2019 Nov Opinions
All (89) | Courses, Education (1) | Meetings (1) | News (6) | Opinions (21) | Top Stories, Tweets (9) | Tutorials, Overviews (45) | Webcasts & Webinars (6)
- Two Years In The Life of AI, Machine Learning, Deep Learning and Java
- Nov 29, 2019.
Where does Java stand in the world of artificial intelligence, machine learning, and deep learning? Learn more about how to do these things in Java, and the libraries and frameworks to use.
-
Open Source Projects by Google, Uber and Facebook for Data Science and AI
- Nov 28, 2019.
Open source is becoming the standard for sharing and improving technology. Some of the largest organizations in the world namely: Google, Facebook and Uber are open sourcing their own technologies that they use in their workflow to the public. - Cartoon: Thanksgiving, Big Data, and Turkey Data Science…
- Nov 28, 2019.
A classic KDnuggets Thanksgiving cartoon examines the predicament of one group of fowl Data Scientists.
- A Doomed Marriage of Machine Learning and Agile
- Nov 28, 2019.
Sebastian Thrun, the founder of Udacity, ruined my machine learning project and wedding.
- Task-based effectiveness of basic visualizations
- Nov 27, 2019.
This is a summary of a recent paper on an age-old topic: what visualisation should I use? No prizes for guessing “it depends!” Is this the paper to finally settle the age-old debate surrounding pie-charts??
- Would you buy insights from this guy? (How to assess and manage a Data Science vendor)
- Nov 25, 2019.
With all the hype from data science vendors selling "actionable insights" to boost your company's bottom line, selecting your analytics partner should proceed through the same, careful process as any traditional business endeavor. Follow these questions and best practices to ensure you manage accordingly.
- Top 8 Data Science Use Cases in Marketing
- Nov 25, 2019.
In this article, we want to highlight some key data science use cases in marketing. Let us concentrate on several instances that present particular interest and managed to prove their efficiency in the course of time.
- Can Neural Networks Develop Attention? Google Thinks they Can
- Nov 25, 2019.
Google recently published some work about modeling attention mechanisms in deep neural networks.
- Advice for New and Junior Data Scientists
- Nov 22, 2019.
If you are a new Data Scientist early in your professional journey, and you’re a bit confused and lost, then follow this advice to figure out how to best contribute to your company.
- The Notebook Anti-Pattern
- Nov 21, 2019.
This article aims to explain why this drive towards the use of notebooks in production is an anti pattern, giving some suggestions along the way.
-
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
- Nov 20, 2019.
The two main takeaways from this paper: firstly, a sharpening of my understanding of the difference between explainability and interpretability, and why the former may be problematic; and secondly some great pointers to techniques for creating truly interpretable models. - Why write for KDnuggets? Calling for original blogs and new authors
- Nov 19, 2019.
KDnuggets is calling for original blogs and contributions from new authors on AI, Data Science, Machine Learning, and related topics. The authors of most popular such blogs in December will be profiled in KDnuggets.
- Reproducibility, Replicability, and Data Science
- Nov 19, 2019.
As cornerstones of scientific processes, reproducibility and replicability ensure results can be verified and trusted. These two concepts are also crucial in data science, and as a data scientist, you must follow the same rigor and standards in your projects.
- Data Science for Managers: Programming Languages
- Nov 19, 2019.
In this article, we are going to talk about popular languages for Data Science and briefly describe each of them.
- On the sensationalism of artificial intelligence news
- Nov 15, 2019.
With artificial intelligence and machine learning now a mainstay of our daily awareness, news organizations have been seen to overstate the reality behind progress in the field. Learn more about recent examples of media hyperbole and explore why this may be happening.
- How I Got Better at Machine Learning
- Nov 13, 2019.
Check out this author's collection of tips and tricks that I learned over the years to get better at Machine Learning.
-
How to Speed up Pandas by 4x with one line of code
- Nov 12, 2019.
While Pandas is the library for data processing in Python, it isn't really built for speed. Learn more about the new library, Modin, developed to distribute Pandas' computation to speedup your data prep. - How Data Analytics Can Assist in Fraud Detection
- Nov 11, 2019.
A primary advantage of data analytics tools is that they can handle massive quantities of information at once. These solutions typically learn what's normal within a collection of information and how to spot anomalies.
- What is Data Science?
- Nov 8, 2019.
Data Science is pitched as a modern and exciting job offering high satisfaction. Does its reality really live up to the hype? Here, we show what it's really like to work as a Data Scientist.
- The Last Defense Against Another AI Winter
- Nov 6, 2019.
My short answer is this: Yes, another AI Winter will be here if you don’t deploy more ML solutions. You and your Data Science teams are the last line of defense against the AI Winter. You need to solve five key challenges to keep the momentum up.
- How to Become a Successful Healthcare Data Analyst
- Nov 5, 2019.
Are you interested in starting your career in the data analysis domain? Read this informative blog on how to get your career off the ground.
