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.
The hidden challenge of Machine Learning
Machine learning has proved to be greatly beneficial for almost every industry. From retail (Amazon), to entertainment (Netflix) and healthcare (Lively), businesses in all kind of industries are using machine learning to enhance their products and services, but also and most importantly, to increase their profits. Coming from a marketing and business background, I personally find machine learning a game changer. That being said, sometimes its overwhelming to think about all the possibilities that something like predictive analytics or natural language processing has to offer.
Raul's new Machine Learning book!
We are proud to announce that Raul Garreta (Co-Founder & CTO of Tryolabs) and Guillermo Moncecchi have published a new book called “Learning scikit-learn: Machine Learning in Python”. Raul has been involved with academia for years, teaching Machine Learning and Natural Language Processing at the Computer Science Institute of Universidad de la República in Uruguay since 2007. As CTO and Product Manager at Tryolabs, he has been applying Machine Learning techniques for the industry since 2009.
Thoughts when considering a Machine Learning project
Very often we get people contacting us for projects in which they envision the usage of some Machine Learning (ML) techniques to solve a specific problem they have. Sometimes these people do not have any automated system and are solving whatever problem they currently have with human labor. In these cases, the mere fact of being able to produce anything that works reasonably well can make a huge difference for them.
Why accuracy alone is a bad measure for classification tasks, and what we can do about it
In a previous blog post, I spurred some ideas on why it is meaningless to pretend to achieve 100% accuracy on a classification task, and how one has to establish a baseline and a ceiling and tweak a classifier to work the best it can, knowing the boundaries. Recapitulating what I said before, a classification task involves assigning which out of a set of categories or labels should be assigned to some data, according to some properties of the data.