Sep2018 Oct Opinions, Interviews
All (52) | Courses, Education (7) | Meetings (5) | News, Features (7) | Opinions, Interviews (14) | Top Stories, Tweets (5) | Tutorials, Overviews (13) | Webcasts & Webinars (1)
- 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.
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How to Create a Simple Neural Network in Python
- 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).