Top 10 Python libraries of 2018
Like in 2015, 2016 and 2017, we’re thrilled to share our hand-picked selection of the top Python libraries (according to our most humble opinion) with you. If you can think of a library that is not mentioned here but you believe deserves to be on the list, please let us know in the comments section at the end of the post. Here we go! 1. slundberg/shap Along with the Deep Learning boom that we’re experiencing, a huge amount of new tools have seen the light.
The major advancements in Deep Learning in 2018
Deep learning has changed the entire landscape over the past few years. Every day, there are more applications that rely on deep learning techniques in fields as diverse as healthcare, finance, human resources, retail, earthquake detection, and self-driving cars. As for existing applications, the results have been steadily improving. At the academic level, the field of machine learning has become so important that a new scientific article is born every 20 minutes.
How we built a stand-in robot for remote workers using IoT and computer vision
With clients and partners located around the globe, we’ve always had a culture of remote collaboration here at Tryolabs. We are used to joining meetings no matter where we are, using tools such as Slack, Google Hangouts, and Zoom. A sweet consequence of this is a generous work from home policy, allowing us to work from home whenever we want. Trouble is, working remotely leads to us missing out on all the fun that takes place outside of meetings when we’re not connected.
How Machine Learning is reshaping Price Optimization
The challenge of setting the right price Setting the right price for a good or service is an old problem in economic theory. There are a vast amount of pricing strategies that depend on the objective sought. One company may seek to maximize profitability on each unit sold or on the overall market share, while another company needs to access a new market or to protect an existing one. Moreover, different scenarios can coexist in the same company for different goods or customer segments.
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 2019
E-commerce and retail companies are leveraging the power of data and boosting sales by implementing recommender systems on their websites. The use cases of these systems have been steadily increasing within the last years and it’s a great time to dive deeper into this amazing machine learning technique. In this blog post, you’ll learn the broad types of popular recommender systems, how they work, and how they are used by companies in the industry.
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.