At: RoninLocation: San Mateo, CA Web: www.projectronin.comPosition: Sr Machine Learning and AI Data Scientist

In all monotonic algorithms that I posted before, I heavily relied on the smbinning::smbinning.custom() function contributed by Herman Jopia as the utility function generating the binning output and therefore feel deeply indebted to his excellent work. However, the availability of smbinning::smbinning.custom() function shouldn’t become my excuse for being lazy. Over the weekend, I drafted a function, e.g. manual_bin(), serving the similar purpose.

A decade ago, machine learning was simply a concept but today it has changed the way we interact with technology. Devices are becoming smarter, faster and better, with Machine Learning at the helm.

StanCon 2018 Helsinki talk slides, notebooks and code have been sometime available in StanCon talks repository, but it seems we forgot to announce this. The StanCon 2018 Helsinki talk list includes also links to videos.

#GAN_Paint: Learn to paint with an AI system:…Generating pictures out of neuron activations – a new, AI-infused photoshop filter…MIT researchers have figured out how to extract more information from trained generative adversarial networks, letting them identify specific ‘neurons’ in the network that correlate to specific visual concepts. They’ve built a website that lets anyone learn to paint with these systems. The effect is akin to having a competent ultra-fast painter standing by your shoulder, letting you broadly spraypaint an area where you’d like, for instance, some sky, and then the software activates the relevant ‘neuron’ in the GAN model and uses that to paint an image for you.  Why it matters: **Demos like this give a broader set of people a more natural way to interact with contemporary AI research, and help us develop intuitions about how the technology behaves.   Paint with an AI yourself here:** GANpaint (MIT-IBM Watson AI Lab website).  Read more about the research here: GAN Dissection: Visualizing and Understanding Generative Adversarial Networks (MIT CSAIL).   Paint with a GAN here (GANPaint website).

By Altexsoft.

Object detection is the identification of an object in the image along with its localisation and classification. It has wide spread applications and is a critical component for vision based software systems. This paper seeks to perform a rigorous survey of modern object detection algorithms that use deep learning. As part of the survey, the topics explored include various algorithms, quality metrics, speed/size trade offs and training methodologies. This paper focuses on the two types of object detection algorithms- the SSD class of single step detectors and the Faster R-CNN class of two step detectors. Techniques to construct detectors that are portable and fast on low powered devices are also addressed by exploring new lightweight convolutional base architectures. Ultimately, a rigorous review of the strengths and weaknesses of each detector leads us to the present state of the art. A Survey of Modern Object Detection Literature using Deep Learning

Objective Bayes Intrinsic Conditional Autoregressive Model for Areal Data (ref.ICAR)Implements an objective Bayes intrinsic conditional autoregressive prior. This model provides an objective Bayesian approach for modeling spatially cor …

Hello Stitch Fix followers, check out where our fellow Stitch Fixers are speaking this month.

At: Ronin Location: San Mateo, CAWeb: www.projectronin.comPosition: Data Engineer