Blog - Igor Muniz - XNV

Written by Igor Muniz Director of Artificial Intelligence

Porto Seguro Challenge - 2nd Place Solution26 October, 2021

We are pleased to announce that we got second place in the Porto Seguro Challenge, a competition organized by the largest insurance company in Brazil. Porto Seguro challenged us to build an algorithm to identify potential buyers of their products. If you are interested in knowing how to do the same and still discover the secrets that led us to second place, read on below! Introduction In this problem, Porto Seguro provided us with data of users who bought or did not buy one of its products. T

VGGNets are back03 June, 2021

If you have studied the deep learning timeline history, you may know that the big moment was in 2012 when Alex Krizhevsky presented a deep convolutional architecture able to improve the state-of-the-art error on ImageNet dataset by 11%. After that, it was proved that deep learning era had come. Two years later a better architecture was proposed by Karen Simonyan and Andrew Zisserman from the University of Oxford. In this model, the main difference was the number of layers, where they have s

Calculating Material Stress Using Deep Learning27 April, 2021

If you are an engineer or get some classes of its subjects in college, you know resistances of material can give you a huge headache! Determining the stress a material is under, for example, requires complex equations created by mathematicians, engineers, and physicists. Such difficult that most part of the time we need computers to solve it. However, it seems that MIT researchers discovered a new method to simplify our lives, and guess what, using AI of course! By applying some computer visio

Nvidia GTC 202112 April, 2021

Nvidia's annual GTC starts today and you don't want to lose this opportunity. Every year Nvidia comes to show their exciting advancements in GPU technology and as artificial intelligence is directly linked to GPUs, you should expect a lot of AI content there, becoming a paradise for data scientist, machine learning engineers, and AI enthusiasts. You can also watch content about automation, health care, and quantum computing, all for free. But if you have extra money, I will recommend doing a wor

Potatoes Cluster: Training a Deep Learning Model Using a 100 Potatoes01 April, 2021

Have you ever heard about battery potatoes? Did you know that it is also possible to process information using potatoes? In this post we are going to show you how it’s possible to create a cluster of potatoes, that not only serves as a power supply but also processes data in a very cheap way. With that, we could achieve the performance of a Nvidia GTX 1060 using 100 potatoes connected with nails and clips. Building the Cluster In order to build our cluster, we are going to need some other item

Distributed learning in an on-premise cluster - A Kaggle Reinforcement Learning case08 January, 2021

Have you tried any distributed learning algorithms? If you are just starting out in this area, I have my doubts, but if you have been on this path for a few years, you might have faced one of those models. The incredible development of the machine learning area in the last decade has not only brought a new state of the art to several problems but has also taken processing optimization and parallelization to another level. With increasingly larger models, any common machine or even a single sup

Multi-task learning: Solving different computer vision problems with a single model12 November, 2020

In the last few years, significant improvements in computer vision were made, making it possible to obtain important information from images. Some of the challenges for a better understanding of a scene are the detection of people and the recognition of the activities they are performing. In this post, I'm going to show a method that I proposed to do a single end-to-end model able to detect people, estimate their pose, and recognize each one of their activities by their pose. Figure 1: Result

7 common mistakes of a machine learning beginner15 October, 2020

In recent years, the term Artificial Intelligence has gained strength and together with it have emerged some professions such as Data Scientist and Machine Learning Engineer. Knowing and applying machine learning is attractive and appears to be the path to success. However this path can be troubled and especially discouraging for those who are just starting out. Over the years working as a Data Scientist and Machine Learning Researcher, I have witnessed several common mistakes that made life di