Getting started with AWS: open source workshop
Introduction One of our strengths at Tryolabs is that we have people coming from diverse technological backgrounds. In order to make sure that everyone who joins the company, no matter their previous experience, can be up to speed with developing apps with the stack we usually use, we have an extensive onboarding process that involves the development of a real application (frontend, backend and some data science), with a coach, code reviews, and iterative improvements.
My PyCon APAC 2018 experience in Singapore
Earlier this year, I was invited to give a keynote talk at PyCon APAC, to be held in Singapore on May 31 – June 2, 2018. It is always an honor to be asked to be a keynote speaker, and this particular conference was taking place in Asia-Pacific – a region which I did not know too much about, since nearly all our clients are based in the US. Eager to explore something different and learn about a new community, I said yes!
Software consulting meets hardware consulting: learnings and opportunities
As a software consulting shop specialized in Machine Learning, the hardware world seems distant. However, at the end of the day, it’s crucial for many activities we do. We’re living in a highly connected world where IoT and other portable devices are gathering more and more data to later be analyzed by algorithms. That said, the hardware and software world will need to come evermore together and we feel that a deeper collaboration between these two sides of the same coin will be necessary.
Introduction to Recommender Systems in 2018
Many e-commerce and retail companies are leveraging the power of data and boosting sales by implementing recommender systems on their websites. In short, these systems aim to predict users’ interests and recommend items that quite likely are interesting for them. Data required for recommender systems stems from explicit user ratings after watching a movie or listening to a song, from implicit search engine queries and purchase histories, or from other knowledge about the users/items themselves.
Announcing Luminoth 0.1: new object detection models, checkpoints and more!
On previous blog posts, we’ve talked about Luminoth, our own open-source computer vision toolkit, built upon Tensorflow and Sonnet. Well, we just released a new version, so this is a good time as any to dive into it! Version 0.1 brings several very exciting improvements: An implementation of the Single Shot Multibox Detector (SSD) model was added, a much faster (although less accurate) object detector than the already-included Faster R-CNN.
Hosting an Object Detection workshop and sponsoring at the PyImageConf
A couple of weeks ago we received an invitation from Adrian Rosebrock to give a workshop at the first edition of the PyImageConf in San Francisco. Our first reaction was of excitement. We were already aware of the great speakers that were featured (François Chollet, Davis King, Adam Geitgey, Adrian Rosebrock himself, among others), and we really liked the format of the event: practical & intimate. Moreover, the conference is centered on three fields we’re pretty familiar with: Deep Learning, Computer Vision & Python.
Introduction to Visual Question Answering: Datasets, Approaches and Evaluation
Historically, building a system that can answer natural language questions about any image has been considered a very ambitious goal. Imagine a system that, given the image below, could answer these questions: What is in the image? Are there any humans? What sport is being played? Who has the ball? How many players are in the image? Who are the teams? Is it raining? Argentina facing England in 1986Image source
Faster R-CNN: Down the rabbit hole of modern object detection
Previously, we talked about object detection, what it is and how it has been recently tackled using deep learning. If you haven’t read our previous blog post, we suggest you take a look at it before continuing. Last year, we decided to get into Faster R-CNN, reading the original paper, and all the referenced papers (and so on and on) until we got a clear understanding of how it works and how to implement it.
Top 10 Python libraries of 2017
December is the time when you sit back and think about the accomplishments of the past year. For us programmers, this is often looking at the open source libraries that were either released this year (or close enough), or whose popularity has recently boomed because they are simply great tools to solve a particular problem. For the past two years, we have done this in the form of a blog post with what we consider to be some of the best work that has been done in the Python community.
Deep Learning for Natural Language Processing (NLP): Advancements & Trends
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.