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New technologies & random stuff.

Introduction to Recommender Systems in 2018

Many e-commerce and retail companies are leveraging the power of data and boosting sales by implementing recommender systems on their websites. In short, these systems aim to predict users’ interests and recommend items that quite likely are interesting for them. Data required for recommender systems stems from explicit user ratings after watching a movie or listening to a song, from implicit search engine queries and purchase histories, or from other knowledge about the users/items themselves.

Announcing Luminoth 0.1: new object detection models, checkpoints and more!

On previous blog posts, we’ve talked about Luminoth, our own open-source computer vision toolkit, built upon Tensorflow and Sonnet. Well, we just released a new version, so this is a good time as any to dive into it! Version 0.1 brings several very exciting improvements: An implementation of the Single Shot Multibox Detector (SSD) model was added, a much faster (although less accurate) object detector than the already-included Faster R-CNN.

Hosting an Object Detection workshop and sponsoring at the PyImageConf

A couple of weeks ago we received an invitation from Adrian Rosebrock to give a workshop at the first edition of the PyImageConf in San Francisco. Our first reaction was of excitement. We were already aware of the great speakers that were featured (François Chollet, Davis King, Adam Geitgey, Adrian Rosebrock himself, among others), and we really liked the format of the event: practical & intimate. Moreover, the conference is centered on three fields we’re pretty familiar with: Deep Learning, Computer Vision & Python.

Introduction to Visual Question Answering: Datasets, Approaches and Evaluation

Historically, building a system that can answer natural language questions about any image has been considered a very ambitious goal. Imagine a system that, given the image below, could answer these questions: What is in the image? Are there any humans? What sport is being played? Who has the ball? How many players are in the image? Who are the teams? Is it raining? Argentina facing England in 1986Image source

Faster R-CNN: Down the rabbit hole of modern object detection

Previously, we talked about object detection, what it is and how it has been recently tackled using deep learning. If you haven’t read our previous blog post, we suggest you take a look at it before continuing. Last year, we decided to get into Faster R-CNN, reading the original paper, and all the referenced papers (and so on and on) until we got a clear understanding of how it works and how to implement it.

Top 10 Python libraries of 2017

December is the time when you sit back and think about the accomplishments of the past year. For us programmers, this is often looking at the open source libraries that were either released this year (or close enough), or whose popularity has recently boomed because they are simply great tools to solve a particular problem. For the past two years, we have done this in the form of a blog post with what we consider to be some of the best work that has been done in the Python community.

Deep Learning for Natural Language Processing (NLP): Advancements & Trends

Over the past few years, Deep Learning (DL) architectures and algorithms have made impressive advances in fields such as image recognition and speech processing. Their application to Natural Language Processing (NLP) was less impressive at first, but has now proven to make significant contributions, yielding state-of-the-art results for some common NLP tasks. Named entity recognition (NER), part of speech (POS) tagging or sentiment analysis are some of the problems where neural network models have outperformed traditional approaches.

TryoSummerParty, 2017 edition

It’s that time of the year again! Time for uncomfortable family meetings, TODO lists that will be quickly forgotten, crazy last minute holiday shopping and, of course, a new edition of the famous #TryoSummerParty. An as-incomplete-as-you-can-get photo of the TryoGang at this year's #TryoSummerParty. If there’s one thing at which we are almost as good as coding, it’s definitely parties and celebrations. So, as we do every year, last weekend we all got together along with our significant others and kids (yes, we are growing up) to celebrate another amazing year at Tryolabs and also look back at all the great things we accomplished in 2017.

Launching Requestium: An integration layer between Requests and Selenium for automation of web actions

From time to time at Tryolabs we have the need of simulating user interactions on websites. To tackle this problem, we usually use Requests, the beloved Python HTTP library, for simple sites; and Selenium, the popular browser automation tool, for sites that make heavy use of Javascript. Using Requests generally results in faster and more concise code, while using Selenium makes development faster on Javascript heavy sites. After writing several of these interactions we found ourselves with the need of writing code that made use of both these approaches at the same time.

Our ODSC talks in video

As we previously announced, the last couple of weeks we’ve been launching Luminoth, our brand new open source toolkit for Computer Vision. As part of this launch, we gave talks in the Open Data Science Conference (ODSC), both at the London and San Francisco editions. The events were a great success, as in 5 days they accumulated around 5000 data scientists, while hosting more than 200 talks from renowned people in the academia and industry.

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