Dear readers and contributors,
as usual, we are delighted to present to you our Weekly Selection of our favorite articles featured on Towards Data Science. We hope you like it 🙂
In Defense of Simplicity, A Data Visualization Journey
By Irene Ros – Reading: 7 min.
The last 8–9 years of my career have been focused on Data Visualization, which has given me plenty of time to develop a philosophy or two about my approach to this field. I say two, but I really mean about half a dozen of semi-distraught career-crisis generating moments (that lasted weeks or months) questioning what it is I want out of this field, whether what I am doing is the "right thing" and whether I should be doing something else.
Machine learning, meet the ocean
By Kate Wing – Reading: 5 min.
It’s a 20 degree day in Medford, Massachusetts and yesterday’s snow is gusting across the road as I cross the river. I’ve flown across the country to gather in a small basement office with a small group of engineers, fishery scientists, and friends recruited with the promise of dinner and energy drinks.
The Next Frontier – From Recognition to Understanding
By Yuri Barzov— Reading: 5 min.
Can AI learn consciousness from a man who lives normal life without 90% of his brain? Maybe an autistic person can help too? What about core values and storytelling? As paradoxical as these questions sound they may give artificial intelligence scientists some important clues to the development of self-aware AI.
Check your assumptions about your data
By Brian Godsey – Reading: 6 min.
Whether we like to admit it (or not), we all make assumptions about data sets. We might assume that our data are contained within a particular time-period. Or, we might assume that the names of the folders that contain emails are appropriate descriptors of the topics or classifications of those emails. These assumptions about the data can be expectations or hopes, conscious or subconscious.
Is LDA a dimensionality reduction technique or a classifier algorithm?
By Meigarom Diego Fernandes – Reading: 9 min.
In this post, I am going to continue discussing this subject, but now, talking about Linear Discriminant Analysis ( LDA ) algorithm. LDA is defined as a dimensionality reduction technique by authors, however some sources explain that LDA actually works as a linear classifier.
Parameter Inference: Maximum Aposteriori & Maximum Likelihood
By Rahul Bohare— Reading: 15 and 10 min.
This post takes an in-depth tour in one of the most important concepts of theoretical Machine Learning, viz., Parameter Inference. I will try to focus on an intuitive understanding of the concept while embedding mathematical formulae as and when I feel the need for them.
