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.
No Sleep till Brooklyn: Our master plan to conquer the East Coast
A pic from one of our visits to Governour Island. We’ve always been fans of the Silicon Valley and the amazing startup culture in California. We’ve been traveling from our engineering HQs in Montevideo to the Bay Area many many times and it has always been very fun and inspiring. Some of us had also the opportunity of visiting East Coast clients in Washington, Boston and NY, and it’s also a blast!
Tryolabs Scholarship for Uruguayan Computer Science students
At Tryolabs we firmly believe that every student who has the passion, determination and commitment to develop a Computer Science career, should have the opportunity to do so, independently of his/her economical condition. In Uruguay we are very proud of our public and free University educational system, which forms top quality engineers and professionals in different fields. As a matter of fact, the vast majority of Tryolabs’ engineers come from the public University (Facultad de Ingeniería, Udelar).
Magazine: A collection of our Machine Learning articles - Get a copy!
Hello everybody, exciting news here: we released our first Machine Learning magazine. We all know it. Machine Learning is undoubtedly one of the most relevant fields in Computer Science and beyond. One can easily state, looking at some data, that this field is now the talk of the town and that we are all somehow touched by it. Although we are all increasingly learning about how Machine Learning – subfield of AI – is changing our day to day, sometimes we might lack some knowledge on how Machine Learning actually works.
List of Machine Learning / Deep Learning conferences in 2018
This year, like the year before, there are a record amount of conferences about Machine Learning worldwide. In order to try to choose which ones we would like to attend, sponsor or submit talks to, we decided to create a structured summary of them. For some conferences we added tags, comments and notable speakers. Please let us know in the comments if we are missing something or to add some information we may be missing.
React Native: a first try
I just came across an old answer I posted in Quora on August 2015 about the future of iOS development. Roughly a year and a half has passed, and things haven’t changed too much to be fair. One of the key points I mentioned in my answer is the ability to speed up the “try” cycle so you don’t have to wait to see your changes. My original bet was on the Apple side, but so far it hasn’t happened.
Pandas & Seaborn - A guide to handle & visualize data in Python
Last Updated: March 2018 Here at Tryolabs we love Python almost as much as we love machine learning problems. These kind of problems always involve working with large amounts of data which is key to understand before applying any machine learning technique. To understand the data, we need to manipulate it, clean it, make calculations and see how variables behave independently, and how they relate to one another. At this post will show how we have been doing this lately.
Building a Chatbot: analysis & limitations of modern platforms
The chatbot industry is still in its early days, but growing very fast. What at first may have looked like a fad or a marketing strategy, is becoming a real need. Would you like to know the movies that are trending in your area, the nearby theaters or maybe watch a trailer? You could use the Fandango bot. Are you a NBA fan trying to get game highlights and updates?