• Tryolabs

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.

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 2017 (and beyond)

We are always trying to stay up to date with the latest research and publications around the world. This includes browsing the raw ArXiv listing (or the saner Arxiv Sanity Preserver), staying up to date with our nerd Twitter feeds and plenty of other sources (mostly /r/MachineLearning) But some things can’t be transmitted via the interwebs, that’s why we like to attend conferences and talks whenever we can. This year, like the year before, there are a record amount of conferences about Machine Learning worldwide.

Pandas & Seaborn - A guide to handle & visualize data elegantly

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?

The major advancements in Deep Learning in 2016

Deep Learning has been the core topic in the Machine Learning community the last couple of years and 2016 was not the exception. In this article, we will go through the advancements we think have contributed the most (or have the potential) to move the field forward and how organizations and the community are making sure that these powerful technologies are going to be used in a way that is beneficial for all.

The 10 main takeaways from MLconf SF

We recently sponsored and attended the MLconf in San Francisco. It was an awesome experience. Congratulations to all the awesome speakers, other sponsors and organizers! During the presentations, we gained a lot of insight from people who are using Machine Learning in the industry. In this post, we attempt to share some of what we learned. 1. It’s (still) not all about Deep Learning It is true that Deep Learning (DL) has had real success in a variety of tasks like image and speech recognition, machine translation, games, and many others.

Machine Learning 101 Meetups

Open events started to take place at our offices under the scope of the Tryolabs Engineering Events. This time, it was the turn of an introductory talk about Machine Learning, which we named Machine Learning 101. The target audience were software developers who knew little to nothing about the field. From the very beginning, we were gladly surprised by the community. A mere 2 hours after posting the event in Meetup.

Tryolabs is Sponsoring MLconf in San Francisco!

Great news over here, we’re honored to confirm that we will be sponsoring the MLconf in San Francisco this year! This event, hosted since 2012, brings together some of the top minds related to Natural Language Processing, Deep Learning, Game Theory, Large-Scale Clustering and many other Machine Learning related fields. By November the 11th, large companies, startups and academics will be gathering together, at the heart of San Francisco, to share views about a common issue: large and noisy data sets.

Chatbots and automated online assistants

The term chatbot is employed generically to refer to a computer program capable of simulating a human conversation through methods of Artificial Intelligence. When we refer to a chatbot, we speak of a tool whose sole purpose is being able to simulate said conversation with the most “human” characteristics possible. One can make a distinction between chatbots and systems that attempt to assist a user (generally a client) by answering questions of performing some tasks.

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