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.
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 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?
Top 10 Python libraries of 2016
Last year, we did a recap with what we thought were the best Python libraries of 2015, which was widely shared within the Python community (see post in r/Python). A year has gone by, and again it is time to give due credit for the awesome work that has been done by the open source community this year. Again, we try to avoid most established choices such as Django, Flask, etc.
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.
Scalable infrastructure in AWS (Part I)
Let’s imagine you have an API running on a single node and you need to implement a new feature that requires some heavy task processing. Obviously you can’t simultaneously process the HTTP request and the task without blocking the web server. Although in Python we have a few alternatives like Celery (check our post on this) or Asyncio (when the heavy task processing is IO bound, future post 😃) to handle this situation, this time we’ll explore a new approach: take advantage of Amazon Web Services (AWS).
Building our site: From Django & Wordpress to a static generator (Part I)
We recently announced the redesign of our website and blog. So far, it has been a great success. The site is a lot faster, SEO is better than ever (signaled by the growth of organic traffic), user bounce rates are down, the amount of visited pages went up as did the duration of the sessions. Hurray! :-) However, the change was much deeper than just a visual revamp. We decided to revise the tech stack that has been powering the site for more than 6 years.