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Deep Face Recognition with Redis - Sefik Ilkin Serengil

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Key value databases come with a high speed and performance where we mostly cannot reach in relational databases. Herein similar to Cassandra, Redis is a fast key value store solution. In this post, we are going to adopt Redis to build an overperforming face recognition application. On the other hand, this could be adapted to NLP studies or any reverse image search case such as in Google Images. The official redis distribution is available for Linux and MacOS here.


Only 50% Of Hires Are Successful: How AI Is Optimizing The Experience

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The pandemic increased unemployment for several months, but finally, companies are seeing re-employment numbers going up fast again. While this is a positive sign, organizations need to have processes to handle the increased number of applicants the best way possible, especially for industries that had to let go of many employees, to recruit the best candidates. Employees are the company's most important asset, so hiring better is critical for your success. I interviewed Yves Lermusi, Chief Futurist at OutMatch, the leading AI-driven hiring platform, to discuss how AI will impact both recruiters and candidates' hiring experience. OutMatch helps leading companies boost talent acquisition performance and to deliver an engaging hiring experience for all.


Letter to a CIO – Understanding your dilemma and how to move forward. Part 2

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This article represents the second part of a series called "Letter to a CIO", which reports the discussions between the author of the letter, dr. Domenico Lepore Founder Intelligent Managemnt Inc. and several Chief Information Officers, with the aim of providing them with an effective methodology to address and successfully solve common problems that CIOs face in the Digital Age. The result of this series of interviews helped dr. A CIO MUST have the abilities necessary to accomplish the transformation from a silo-based Hierarchy to whole system optimization. Without this ability, CIOs will very soon become a relic, something that can be easily disposed of.


Using AI to classify a book

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We are going to work on a specific sub-task of NLP called text classification, this is the process of recognizing a pattern in a text and assign it a label. Examples that are used in your day to day life without you even noticing it include spam detection (in your mailbox), sentiment analysis (when you review a product or leave a comment) and tagging customer queries (when you fill in a contact form on a website). What we will try to do is to classify science-fiction books into different subgenres (dystopia, cyberpunk, space opera, …) based on their plot. In the end, we want a model that is able to take a book plot as an input and output the subgenres detected in the text and the confidence of the model that a subgenre is detected. The demonstrator can take up to 1 minute to open because I use a free version of Heroku to host my app, thus it goes to sleep when nobody uses it and it's better for the planet! This kind of algorithms could help an online market place to classify the books they receive to make more performant recommendations or a librarian to organize originally the books by subgenres instead of alphabetically, to create an experience in the library. Data is one of the most important (if not the most important) thing in data science.


Council Post: Deep Learning? Sometimes It Pays To Go Shallow

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Deep learning is the current darling of AI. Used by behemoths such as Microsoft, Google and Amazon, it leverages artificial neural networks that "learn" through exposure to immense amounts of data. By immense we mean internet-scale amounts -- or billions of documents at a minimum. If your project draws upon publicly available data, deep learning can be a valuable tool. The same is true if budget isn't an issue.


AbbVie Accelerates Natural Language Processing

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AbbVie is a research-based biopharmaceutical company that serves more than 30 million patients in 175 countries. With its global scale, AbbVie partnered with Intel to optimize processes for its more than 47,000 employees. This whitepaper highlights two use cases that are important to AbbVie's research. The first is Abbelfish Machine Translation, AbbVie's language translation service based on the Transformer NLP model, that leverages second-generation Intel Xeon Scalable processors and the Intel Optimization for TensorFlow with Intel oneAPI Deep Neural Network Library (oneDNN). AbbVie was able to achieve a 1.9x improvement in throughput for Abbelfish language translation using Intel Optimization for TensorFlow 1.15 with oneAPI Deep Neural Network Library when compared to TensorFlow 1.15 without oneDNN.1


A robot able to 'hear' through the ear of a locust

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The interdisciplinary study was led by Idan Fishel, a joint master student under the joint supervision of Dr. Ben M. Maoz of the Iby and Aladar Fleischman Faculty of Engineering and the Sagol School of Neuroscience, Prof. Yossi Yovel and Prof. Amir Ayali, experts from the School of Zoology and the Sagol School of Neuroscience together with Dr. Anton Sheinin, Idan, Yoni Amit, and Neta Shavil. The results of the study were published in the journal Sensors. The researchers explain that at the beginning of the study, they sought to examine how the advantages of biological systems could be integrated into technological systems, and how the senses of dead locust could be used as sensors for a robot. "We chose the sense of hearing, because it can be easily compared to existing technologies, in contrast to the sense of smell, for example, where the challenge is much greater," says Dr. Maoz. "Our task was to replace the robot's electronic microphone with a dead insect's ear, use the ear's ability to detect the electrical signals from the environment, in this case vibrations in the air, and, using a special chip, convert the insect input to that of the robot."


SVM Classifier and RBF Kernel -- How to Make Better Models in Python

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It is essential to understand how different Machine Learning algorithms work to succeed in your Data Science projects. I have written this story as part of the series that dives into each ML algorithm explaining its mechanics, supplemented by Python code examples and intuitive visualizations. Support Vector Machines (SVMs) are most frequently used for solving classification problems, which fall under the supervised machine learning category. The exact place of these algorithms is displayed in the diagram below. Let's assume we have a set of points that belong to two separate classes.


Classification with Localization: Convert any Keras Classifier to a Detector

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Image classification is used to solve several Computer Vision problems; right from medical diagnoses, to surveillance systems, on to monitoring agricultural farms. There are innumerable possibilities to explore using Image Classification. If you have completed the basic courses on Computer Vision, you are familiar with the tasks and routines involved in Image Classification tasks. Image Classification tasks follow a standard flow – where you pass an image to a deep learning model and it outcomes the class or the label of the object present. While learning Computer Vision, most often a project that would be equivalent to your first hello world project, will most likely be an image classifier. You attempt to solve something like the digit recognition on MNIST Digits dataset or maybe the Cats and Dog Classification problem.


Global Big Data Conference

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As more companies adopt AI, the risks posed by AI are becoming clearer to business leaders. That is driving many companies to hire AI ethicists to help guide them through an ethical minefield. But just as data scientists proved to be as elusive as unicorns, qualified AI ethics are also in very short supply, says Beena Ammanath, executive director of Deloitte's AI Institute. "We've seen different models evolving. It's still very nascent," Ammanath tells Datanami.