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OUR BLOG

New technologies & random stuff.

Deep Learning for NLP, advancements and trends in 2017

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.

And the Tryolabs Scholarship goes to...

Earlier this year we announced the creation of the Tryolabs Scholarship, aimed to support university students at early stages of their careers in computer science and engineering. The experience as a whole was amazing and we selected a winner and a runner-up a couple of months ago. Now that things settled down, we are ready to tell you more about them. And Tryolabs Scholarship goes to… (drum roll please) Let’s start by introducing two outstanding people, examples of hard work, dedication and determination.

Launching Luminoth: our open source computer vision toolkit

After a few months working in stealth mode, we are very proud to launch our Deep Learning initiative: luminoth.ai Luminoth is an open source toolkit for computer vision. Currently, we support object detection and image classification, but we are aiming for much more. It is built in Python, using Google’s Machine Intelligence framework, TensorFlow; and Sonnet, a very useful library built by DeepMind for building complex neural networks with reusable components.

Tryolabs Working Trip NYC, in pictures

Our commitment at Tryolabs is to create the best hi-tech company to be part of. We believe this is done by creating the best opportunities in terms of professional and personal growth for our team. This year we decided to do our first “Tryolabs working trip”. We rented a house in Brooklyn for one month for our team to visit and work from there. Read more about it here. The idea in a nutshell:

We'll be speaking at ODSC San Francisco & London, presenting our Deep Learning R&D findings

As we have become accustomed to, exciting things are happening in the Machine Learning ecosystem. One can easily argue that it is novel applications of Deep Learning which are leading this excitement. Real world uses of Deep Learning are growing day to day: improving machine text translation, music generation, style transfer, object detection and cooler than ever generative models are just a few examples. The vast number of new applications, and the pace of improvement over existing ones, make it harder than ever to keep up to date with the latest advancements in the field.

Object detection: an overview in the age of Deep Learning

There’s no shortage of interesting problems in computer vision, from simple image classification to 3D-pose estimation. One of the problems we’re most interested in and have worked on a bunch is object detection. Like many other computer vision problems, there still isn’t an obvious or even “best” way to approach the problem, meaning there’s still much room for improvement. Before getting into object detection, let’s do a quick rundown of the most common problems in the field.

Finding the right representation for your NLP data

When considering what information is important for a certain decision procedure (say, a classification task), there’s an interesting gap between what’s theoretically —that is, actually— important on the one hand and what gives good results in practice as input to machine learning (ML) algorithms, on the other. Let’s look at sentiment analysis tools as an example. Expression of sentiment is a pragmatic phenomenon. To predict it correctly, we need to know both the meaning of the sentences and the context in which those sentences appeared.

Deep Learning, Computer Vision, NLP, Python, Infrastructure & more.

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