The wonders of data science are somewhat inconceivable to many of us. One of the main reasons is the fact that its benefits, applications and procedures are unknown to the vast majority of people.
Therefore, the idea of data science as a whole, ends up in a mixture of concepts within people’s collective imagination, such as big data, artificial intelligence, data mining and many more.
This highlights the importance of conceptualizing and summarizing the work of data scientists, to facilitate comprehension of the numerous achievements in this field.
With this goal in mind, today we present a recap of the paper of Felipe Calderero, one of the most renowned professionals in the area of data science and artificial intelligence. He is also the Director of the Master’s Program in Data Science at Nuclio Digital School.
If you’re eager to learn more about it, you can analyze the full article here. Without further ado, let’s dive into it!
What’s this hybrid model and what is it useful for?
Extracting information in depth from images or videos is a critical task for many applications, such as autonomous vehicles, 3D movies, 3D reconstruction and many others.
So far, many approaches have been proposed in the literature to address this problem, from classical models (e.g., models based on differential equations) to deep learning models based on convolutional networks.
This article proposes a hybrid model that leverages the advantages of both approaches. In other words, the deep learning part for automatic extraction of relevant features in the image (feature selection), and a part that takes advantage of the generalization capabilities of classical models to extrapolate data.
The experimental results are evaluated for a large set of images (the public KITTI Depth Completion Suite image database). It demonstrates that these models perform better than similar state-of-the-art models.
Keeping this in mind, it is crystal clear that these tools can be applied to a whole lot of different areas. These kinds of achievements are nothing but the tip of the iceberg of the wonders of data science.
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How can I become a data scientist myself?
As we’ve stated before, these sorts of achievements are just an example of the many great things that can be accomplished within the field of data science.
For instance, it can be used in predictive maintenance, where analyzing data can prevent accidents and save tons of money. It can also be useful towards customer segmentation, through the use of data analytics, which can improve marketing campaigns and customer satisfaction. Not to mention its usefulness in analyzing the behavior of society and its overall sentiment towards different phenomena.
Are you intrigued by AI and looking for a career in this area? It’s as easy as joining the Master in Data Science program of Nuclio Digital School, in which you will learn everything about these concepts, in a practical way and within a few months.
Challenge yourself and join the digital revolution! #StartUpYourLife
