Feedback Sequence-to-Sequence Model – Gonna Reverse Them All!

This tutorial assumes that you have a pretty good understanding about the basics of Recursive Neural Networks and Backpropagation Through Time (BPTT) and how these models actually work Terminologies One-to-one: Problems that are concerned into getting a direct relation between an input word and output word. For example, the relation (like, love) is considered to […]

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Deep Learning Paper Sparks Online Feud!

Feature image is created by Jannoon028 – Freepik.com Researchers Yoav Goldberg and Yann Lecun face off on Natural Language Processing Social media is humanity’s new intellectual battlefield. Sports fans, social justice warriors, and even the President of the United States tweet, take to discussion boards or make memes to mock, preach, thrust and parry against […]

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Epic’s Tim Sweeney: Deep Learning A.I. Will Open New Frontiers in Game Design

“[Video game] AI is still in the dark ages,” Epic CEO Tim Sweeney told a crowd gathered for Games Beat’s 2017 industry summit. The video game industry has witness a tremendous amount of growth, thanks to the incredible increase in computation power in terms of visual representations. Using the parallel computation ability of GPUs, powerful […]

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[Thesis Tutorials II] Understanding Word2vec for Word Embedding II

Previously, we talked about Word2vec model and its Skip-gram and Continuous Bag of Words (CBOW) neural networks. Regularly, when we train any of Word2vec models, we need huge size of data. The number of words in the corpus could be millions, as you know, we want from Word2vec to build vectors representation to the words […]

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[Thesis Tutorials I] Understanding Word2vec for Word Embedding I

Vector Space Models (VSMs): A conceptual term in Natural Language Processing. It represents words as set of vectors, these vectors are considered to be the new identifiers of the words. These vectors are used in the mathematical and statistical models for classification and regression tasks. Also, they shall be unique to be able to distinguish between […]

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Women Tech Markers With Yael Karov of Ginger

Today we published the second episode of GDL-IL Women Tech-makers with Yael Karov (Founder and CEO of Ginger Software). Gingre is a service built from Karov’s 20+ years of experience in the field of natural language processing and machine learning that helps users improve their online English language communication. Here the discussion was with Michal Segalov and […]

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GDL Israel – Women Techmakers

In the past few weeks we had the pleasure to host in Google two amazing women that lead by showing how you can innovate in the tech space. The first conversation was with Rony Ross (Founder, Executive Chairman and Chief Technology Officer of Panorama Software Ltd). It was an interesting discussion between Daniela Raijman-Aharonov (Engineering Manager […]

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Home Depot Product Search Relevance, Winners’ Interview: 2nd Place | Thomas, Sean, Qingchen, & Nima

The Home Depot Product Search Relevance competition challenged Kagglers to predict the relevance of product search results. Over 2000 teams with 2553 players flexed their natural language processing skills in attempts to feature engineer a path to the top of the leaderboard. In this interview, the second place winners, Thomas (Justfor), Sean (sjv), Qingchen, and […]

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Home Depot Product Search Relevance, Winners’ Interview: 3rd Place, Team Turing Test | Igor, Kostia, & Chenglong

The Home Depot Product Search Relevance competition which ran on Kaggle from January to April 2016 challenged Kagglers to use real customer search queries to predict the relevance of product results. Over 2,000 teams made up of 2,553 players grappled with misspelled search terms and relied on natural language processing techniques to creatively engineer new […]

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Home Depot Product Search Relevance, Winners’ Interview: 1st Place | Alex, Andreas, & Nurlan

A total of 2,552 players on over 2,000 teams participated in the Home Depot Product Search Relevance competition which ran on Kaggle from January to April 2016. Kagglers were challenged to predict the relevance between pairs of real customer queries and products. In this interview, the first place team describes their winning approach and how […]

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