Decision Trees in R

Learn all about decision trees, a form of supervised learning used in a variety of ways to solve regression and classification problems.

Create TensorFlow Name Scopes For TensorBoard

Use TensorFlow Name Scopes (tf.name_scope) to group graph nodes in the TensorBoard web service so that your graph visualization is legible

Want to Build an AI Product? Data is Key

Currently, we are seeing three waves of artificial intelligence: expert systems, machine learning, and goal-based AI. The first wave consists of expert systems which are rule-based, very narrow and rigid, have zero learning capabilities, and are poor in the real world. The second wave consists of machine learning which utilizes probabilistic and statistical techniques and is good at classifying and predicting. However, it has limited ability to understand context and needs a lot of data to continuously learn and improve. The third wave is goal-based AI which is a futuristic and contextual adaptation. During this wave, AI has the ability to understand context and reason, and it requires less training data.

Data Science Predicting The Future

Predictive analytics in data science rest on the shoulders of explanatory data analysis, which is precisely what we were discussing in our previous article – The What, Where and How of Data for Data Science. We talked about data in data science, and how business intelligence (BI) analysts use it to explain the past. In fact, everything is connected. Once the BI reports and dashboards have been prepared and insights – extracted from them – this information becomes the basis for predicting future values. And the accuracy of these predictions lies in the methods used. Recall the distinction between traditional data and big data in data science or refer to our first article on the What-Where-How of Data science. We can make a similar distinction regarding predictive analytics and their methods: traditional data science methods vs. Machine Learning. One deals primarily with traditional data, and the other – with big data.

R vs Python: Image Classification with Keras

Even though the libraries for R from Python, or Python from R code execution existed since years and despite of a recent announcement of Ursa Labs foundation by Wes McKinney who is aiming to join forces with RStudio foundation, Hadley Wickham in particularly, (find more here) to improve data scientists workflow and unify libraries to be used not only in Python, but in any programming language used by data scientists, some data professionals are still very strict on the language to be used for ANN models limiting their dev. environment exclusively to Python.

Most Starred R Packages on GitHub

It seems like all the best R packages proudly use GitHub and have a README adorned with badges across the top. The recent Microsoft acquisition of GitHub got me wondering: What proportion of current R packages use GitHub Or at least refer to it in the URL of the package description. Also, what is the relationship between the number of CRAN downloads and the number of stars on a repository My curiosity got the best of me so I hastily wrote a script to pull the data. Click here to go straight to the full script and data included at the bottom of this post. I acknowledge there are more elegant ways to have coded this, but let´s press on.

Effectively scaling Shiny in enterprise

Scalability is a hot word these days, and for good reason. As data continues to grow in volume and importance, the ability to reliably access and reason about that data increases in importance. Enterprises expect data analysis and reporting solutions that are robust and allow several hundred, even thousands, of concurrent users while offering up-to-date security options. Shiny is a highly flexible and widely used framework for creating web applications using R. It enables data scientists and analysts to create dynamic content that provides straightforward access to their work for those with no working knowledge of R. While Shiny has been around for quite some time, recent introductions to the Shiny ecosystem make Shiny simpler and safer to deploy in an enterprise environment where security and scalability are paramount. These new tools in connection with RStudio Connect provide enterprise grade solutions that make Shiny an even more attractive option for data resource creation.