NILG.AI

We are delighted to announce our forthcoming workshop on Self-Supervised Learning, a pivotal event slated to take place during the XIII Symposium on Bioengineering, the largest Portugurese Bioengineering congress!

The Symposium on Bioengineering is a beacon of collaboration, organized by the Bioengineering Students’ Association, and caters to students and researchers alike. It stands firmly on three fundamental pillars: the relentless promotion of scientific excellence and innovation, the construction of global bridges connecting participants, speakers, and diverse facets of Bioengineering, and a steadfast commitment to facilitating accessible and dynamic knowledge sharing.

What is Self-supervised Learning?

For those who cannot attend our Self-supervised learning workshop, here is a short definition of Self-supervised learning.

Self-supervised learning is a machine learning paradigm where a model learns to represent and understand data by generating its own supervision signal from the input data, without relying on external labels or annotations. In traditional supervised learning, you typically have a dataset with labeled examples, where each data point is associated with a target or label that the model aims to predict. In contrast, self-supervised learning aims to leverage the inherent structure or information within the data itself to create training signals.

The key idea behind self-supervised learning is to design a task or objective that can be framed using the data itself, often as a pretext task. This pretext task doesn’t require external labels and is a proxy for learning useful representations. Once the model is trained on this pretext task, the learned representations can be transferred to downstream tasks where labeled data may be scarce or expensive to obtain.

We will present how we are using this concept at NILG.AI projects successfully.

NILG.AI and Bioengineering

On our side, unlocking business capabilities using Data Intelligence is our motto. Thus, we have been able to do it worldwide in a wide range of fields that intersect with Bioengineering! Some of those examples include support for medical diagnosis, optimization of internal processes in hospitals, and validation of compliance in medical environments. 

Course

The Machine Learning Spectrum

Master self-supervised learning and other advances ML techniques in our course.

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To accomplish these feats, we’ve assembled a dedicated team of data science experts who continually strive for new knowledge and employ cutting-edge methodologies while embracing the vibrant culture of NILG.AI.

For those eager to delve deeper into AI or seek a collaborative partner in corporate training initiatives, we extend an invitation to contact us or stay updated through our social media channels. Exciting developments are on the horizon, and we are eager to embark on this journey with you. Together, we’ll chart new frontiers in the ever-evolving landscape of AI and Bioengineering.