To see what kind of talent we are currently looking for and submit your resume, please visit: https://a9.com/careers/ We are always looking for talented people with backgrounds in: · Computer Vision · Machine Learning · Natural Language Processing · Backend Infrastructure / Systems Software Development · Analytics Data Mining · Pattern Recognition · Artificial Intelligence · Optical Character Recognition · Server Infrastructure · Augmented Reality · DevOps / Operations Engineer · Software Developer in Test A9 solves some of the biggest challenges in search and advertising. We focus on helping people find the things they want. We design, develop, and deploy high performance, fault-tolerant distributed search systems used by millions of Amazon customers every day. Our Search Relevance team works to maximize the quality and effectiveness of the search experience for visitors to Amazon websites worldwide.

I've been making some TensorFlow examples for my website, fomoro.com, While it's fresh in my head, I wanted to write up an end-to-end description of what it's like to build a machine learning app, and more specifically, how to make your own reverse image search. For this demo, the work is ⅓ data munging/setup, ⅓ model development and ⅓ app development. At a high-level, I use TensorFlow to create an autoencoder, train it on a bunch of images, use the trained model to find related images, and display them with a Flask app. In the last post, I talked model development and training.

Putting outfits together is fun, but it could also be irritating to do every single day. Something like Cher's virtual wardrobe in Clueless could help -- or Pinterest Lens, which the social network has just upgraded to be a much better stylist. The company says it made major improvements to give its image recognition tool the capability to make outfits based on specific pieces of clothing or accessories you already own. If you have a denim jacket that you don't know what to do with, for instance, you can upload a picture and look at the sample OOTDs Lens shows you. The tool can also recommend new clothes to buy based on what you usually wear.

This folder contains two notebooks which demonstrate use of CTPN (Caffe implementation) [1,2] for Text box detection and CRNN (PyTorch implmentation) [3,4] for Text character recognition. Most online tutorials describe traditional OCR techniques using Tessaract. However Tessaract is not useful for scene text recognition, i.e. text occurring in natural scenes, with wide variation in fonts, colors and background. Over the last couple of years significant improvements have been made in using deep learning for OCR, in this demo we will show how you can use a textbox detector and a text recognition model to perform OCR on scene text. Its possible to get good out-of-box performance without any having to perform any fine-tuning.

Seek data visualization solutions that leverage pattern recognition algorithms for individual devices as well as device groups. For example, if you want to analyze a conveyor belt's behavior over the past month, the software should provide an algorithm designed to analyze the operational state of conveyor belts. Bear in mind that it doesn't make sense to apply the same algorithm across all of your devices, because each type of asset – indeed each machine – behaves in a distinct manner.

Google has made the app open-source so any developer can adopt it. It can perform chores like object detection, face attribute recognition, fine-grained classification (recognizing a dog-breed, for instance) and landmark recognition. The tech is part of TensorFlow, Google's deep learning model that recently shrunk down to mobile size in a new version called TensorFlow Lite. MobileNets is not one-size-fits-all, as Google has actually built 16 pre-trained models "for use in mobile projects of all sizes." The larger the model, the better it is at recognizing landmarks, faces or doggos, with the most CPU-intensive ones hitting scores of between 70.7 and 89.5 percent accuracy.

The application of pattern recognition technology to large datasets has revolutionised the digital economy. But digital represents only 5% of GDP in OECD countries: the remaining 95% is still largely untouched by data science (DS). The larger "old economy" companies are just beginning their data journey and data science is yet to be institutionalised: Outside the tech leviathans DS is still a cottage industry with artisan DS crafting bespoke prototypes to their own standards. If DS is to fulfil its promise, it needs to industrialise. This blog explains what I mean by this, and proposes a number of issues which must be addressed if it is to do so. Most DS blogs are technical: algorithms, distributed computation, visualisation etc. The rest are case studies of projects where these techniques are applied to a domain.

Google is poised to begin a grand experiment in using machine learning to widen access to healthcare. If it is successful, it could see the company help protect millions of people with diabetes from an eye disease that leads to blindness. Last year researchers at the search and ads company announced that they had trained image recognition algorithms to detect signs of diabetes-related eye disease roughly as well as human experts. The software examines photos of a patient's retina to spot tiny aneurisms indicating the early stages of a condition called diabetic retinopathy, which causes blindness if untreated. At the 2017 WIRED Business Conference in New York City today, a leader of Google's project said that work has begun on integrating the technology into a chain of eye hospitals in India.

Machine learning (ML) has received a lot of attention recently, and not without good reason. It has already revolutionized fields from image recognition to healthcare to transportation. "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E." Not very clear, is it? This post, the first in a series of ML tutorials, aims to make machine learning accessible to anyone willing to learn.

A while back, I was reading an article posted on Facebook, about Clovis people found alive and well living in Florida, with a picture featuring tribesmen (see below.) The quality of the picture was poor, and the URL was very suspicious: baynews9.com.ddwg.clonezone.link, as to make it appear that it was from Baynews9.com. It turned out that the picture (and thus the whole story) was fake: these people are real people living in Peru, see here for a Youtube video about them. My question is how to detect that a story is fake? The picture might have metadata embedded in it, allowing the data scientist to find the real source, unless it is a screenshot.