Tag: Deep Learning

Turning classes into inputs

Let’s face it, we all have worked on an ML project where we had to predict a ridiculously high number of classes. Large enough to make the number of observations per class into an embarrassingly small subset. Most people model these tasks as a multiclass classification problem where, for each input observation, we must predict […]

Written by on Sep 22, 2022

Teaching Models With Free Data

“The more I see, the less I know” might be a saying, but it does not apply to AI models. It’s well known that the performance of an artificial neural network is highly dependent on the volume and on the diversity of the data that was shown to the model. This happens because exposing the […]

Written by on Feb 23, 2022

Multiple Product Forecasting in the construction industry

In this article, we will cover a use case in the construction industry related to forecasting the needed materials for construction and the time in which they will be required. In the construction industry, there is a lot of uncertainty between the order time and the time in which it is actually executed, due to […]

Written by on Nov 9, 2021

Address validation for optimizing delivery success rate

In recent years, the e-commerce industry has been increasing significantly in several sectors like food, retailing, and electronics. In the latter part of 2020, this increase has been even higher with growth 3 times greater than the previous years, mostly due to the pandemic context we live in, which boosted the number of online shopping. […]

Written by on Mar 17, 2021

An AI-Based Image Content Retrieval System

Similarity measurement is the basis for any information retrieval, management, or data mining system. Both in industry and in the scientific community, similarity detection has been shown to be extremely useful when applied to different use cases. Over time, the information available on the internet has been growing in an exponential way, making it harder […]

Written by on Mar 7, 2021

Embedding Domain Knowledge

In the good old days, working as a Machine Learning Engineer meant working 95% of the time on feature engineering and 5% on training models with the extracted features. This was a manually intensive and time-consuming process, that usually led to inflexible proofs of concept that could hardly be adapted to new settings. Fortunately, Deep […]

Written by on Feb 17, 2021

Difficult Targets to Optimize: the ROC AUC

In many binary classification problems, especially in domains with highly unbalanced problems (such as the medical domain and rare event detection), we need to make sure our model does not become too biased for the more predominant class.  Thus, you may have heard that accuracy is not a good metric to validate classifiers in unbalanced […]

Written by on Dec 18, 2020