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Introduction To YolactEdge For Real-time Object Segmentation On Edge Device


YolatEdge is one of the first competitive instanced segmentation techniques that can run on small devices with great real-time speed, It can reach up to 30fps on Nvidia Jetson AGX Xavier and 172fps on RTX 2080Ti. YolactEdge techniques come with Resnet-101 backbone which takes 550 550 resolution image as input. It paper called YolactEdge: Real-time Instance Segmentation on the Edge is authored by Haotian Liu, Rafael A. Rivera Soto, Fanyi Xiao, and Yong Jae Lee in Dec 2020, and the code and models are open-sourced on GitHub here. In order to do inferences in real-time speeds on edge devices, the authors built the SOTA image-based real-time instances segmentation method YOLACT and did some new changes mainly two: one at algorithms level and other system levels. YolactEdge leverages the facility of Nvidia TensorRT machine inference engine to quantize the network parameters to fewer bits while systematically balancing any tradeoff inaccuracy, and it also leverages temporal redundancy in the video, and learn to rework and propagate features over time in order that the deep network's expensive backbone feature computation doesn't get to be fully computed on every frame.

Chief.AI launches pay-as-you-go AI platform for drug discovery


Machine learning operations platform Chief.AI, in partnership with the Medicines Discovery Catapult, has launched the first no-code, pay-as-you-go artificial intelligence (AI) platform for drug discovery. The project is supported by a grant from Innovate UK, and will give small and medium-sized enterprises (SMEs) cloud-based access to AI models and data for drug discovery, diagnostics and clinical trials. Chief.AI's platform is the first no-code service of its type in the bioinformatics space, meaning SMEs can build applications quickly and without substantial internal IT capabilities. The use of AI in drug discovery is growing rapidly, and is expected to transform the pharmaceutical industry more than any other emerging technology. Chief.AI's platform allows SMEs to affordably access cutting-edge technology to minimise the hit-and-miss nature of drug discovery and identify novel therapies with enhanced speed and accuracy, the Manchester-based company said.

House Price Forecasting using Zillow Economics dataset


In the previous blog, we discussed a predictive model for house prices using Machine Learning algorithms. In this blog, we are going to discuss the time series forecasting on Zillow economics data using a statistical modeling approach. The project was implemented in September 2019 and forecasting of house prices was done for the next year that is 2020. The code could be reused by changing the span of forecasting that is year for forecasting or duration of forecasting. The results discussed in this blog are for the year 2020.

Monte Carlo Markov Chain (MCMC) explained


MCMC methods are a family of algorithms that uses Markov Chains to perform Monte Carlo estimate. The name gives us a hint, that it is composed of two components -- Monte Carlo and Markov Chain. Let us understand them separately and in their combined form. Monte Carlo method derives its name from a Monte Carlo casino in Monaco. It is a technique for sampling from a probability distribution and using those samples to approximate desired quantity. In other words, it uses randomness to estimate some deterministic quantity of interest.

How to Optimize a Deep Learning Model


Hyperparameter optimization is a critical part of deep learning. Just selecting a model is not enough to achieve exceptional performance. You also need to tune your model.

Operationalizing Machine Learning for the Automotive Future


It's no secret that global mobility ecosystems are changing rapidly. Like so many other industries, automakers are experiencing massive technology-driven shifts. The automobile itself drove radical societal changes in the 20th century, and current technological shifts are again quickly restructuring the way we think about transportation. The rapid progress in AI/ML has propelled the emergence of new mobility application scenarios that were unthinkable just a few years ago. These complex use cases require some rigorous MLOps planning.

Main concepts behind Machine Learning


Imagine you are teaching a kid to differentiate dogs from cats: at first, you show him many images of both animals, identifying each of them. With these examples, he can associate each animal with its name and then classify new images correctly. The supervised learning has exactly the same idea: from a big train dataset, the algorithm "learns" the relationship between data and label and, therefore, it can predict the result of any other input. In mathematical terms, we are trying to find a expression Y f(X) b that can predict the results. Where X is the input, Y is the prediction and f(X) b is the model learned by the algorithm.

ggforce: Make a Hull Plot to Visualize Clusters in ggplot2


The ggforce package is a ggplot2 extension that adds many exploratory data analysis features. In this tutorial, we'll learn how to make hull plots for visualizing clusters or groups within our data. This article is part of R-Tips Weekly, a weekly video tutorial that shows you step-by-step how to do common R coding tasks. Here are the links to get set up. Learn how to use ggforce in our 7-minute YouTube video tutorial. The Hull Plot is a visualization that produces a shaded areas around clusters (groups) within our data.