wrapr 1.6.2 is now up on CRAN. We have some neat new features for R users to try (in addition to many earlier wrapr goodies).

Big news: Dataiku 5.0 is here, and it has a fresh new look and feel. But it’s not just cosmetics - the latest release introduces a suite of powerful features to help organizations take the plunge into Enterprise AI. 

Hurricane Florence is forecast to touch down Thursday night or Friday, and what’s become the norm, there are several ways to see where the hurricane is and where it might go. Here are a handful of views. Each focuses on different aspects of potential storm.

“There’s the part you’ve braced yourself against, and then there’s the other part” – The Mountain Goats

Active learning is a teaching technique that involves the students in the learning process. It is in contrast to traditional learning methods of lectures where students passively receive information without taking measures to ensure they have sufficiently understood the material. In other words, active learning involves getting students to do activities but also gets them to think about the purpose behind these activities[1]. Examples of active learning involve classroom discussions and short interactive exercises. This blog post will first further discuss active learning and its benefits and then discuss how we use active learning techniques in our curriculum.

Create and Visualize Hillshaded Maps from Elevation Matrices (rayshader)Uses a combination of raytracing, spherical texture mapping, lambertian reflectance, and ambient occlusion to produce hillshades of elevation matrices. …

Artificial Intelligence, Machine Learning and Big Data – A Comprehensive Report

The secret is out: Nina Zumel and I are busy working on Practical Data Science with R2, the second edition of our best selling book on learning data science using the R language.

AlphaX We present AlphaX, a fully automated agent that designs complex neural architectures from scratch. AlphaX explores the exponentially exploded search space with a novel distributed Monte Carlo Tree Search (MCTS) and a Meta-Deep Neural Network (DNN). MCTS intrinsically improves the search efficiency by automatically balancing the exploration and exploitation at each state, while Meta-DNN predicts the network accuracy to guide the search, and to provide an estimated reward for the preemptive backpropagation in the distributed setup. As the search progresses, AlphaX also generates the training date for Meta-DNN. So, the learning of Meta-DNN is end-to-end. In searching for NASNet style architectures, AlphaX found several promising architectures with up to 1% higher accuracy than NASNet using only 17 GPUs for 5 days, demonstrating up to 23.5x speedup over the original searching for NASNet that used 500 GPUs in 4 days. …

In this post we are going to develop a java face recognition application using deeplearning4j. The application is offering a GUI and flexibility to register new faces so feel free to try with your own images. Additionally you can check out the free open source code as part of the PactPub video course Java Machine Learning for Computer Vision together with many new improvements to previous posts applications in java.