Synthetic data case study

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From: Rafael A.
Added: 13.04.2021
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While mature algorithms and extensive open-source libraries are widely available for machine learning practitioners, sufficient data to apply these techniques remains a core challenge. Discover how to leverage scikit-learn and other tools to generate synthetic data appropriate for optimizing and fine-tuning your models. It is becoming increasingly clear that the big tech giants such as Google, Facebook, and Microsoft are extremely generous with their latest machine learning algorithms and packages they give those away freely because the entry barrier to the world of algorithms is pretty low right now. This often becomes a thorny issue on the side of the practitioners in data science DS and machine learning ML when it comes to tweaking and fine-tuning those algorithms. It will also be wise to point out, at the very beginning, that the current article pertains to the scarcity of data for algorithmic investigation, pedagogical learning, and model prototyping, and not for scaling and running a commercial operation.
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RarePlanes: A Case Study in Accelerating Deep Learning With Synthetic Data

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Case Study: Toyota Makes Mixed Reality Magic – Immersive Learning News

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Synthetic data

Log in. Hi [[ session. While computer vision has vast potential across industries, its growth has been limited by data constraints. Reverie to empirically test synthetic data in algorithm training -- and found that it worked. Join this webinar to learn: - How to bootstrap your algorithm and improve accuracy when real data is limited or nonexistent - The relative performance of AI models trained on real vs synthetic data - The real-world applications across industries - Key features of RarePlanes, now the largest open dataset of real and synthetic overhead imagery.
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Suggestions or feedback? Previous image Next image. Although data scientists can gain great insights from large data sets — and can ultimately use these insights to tackle major challenges — accomplishing this is much easier said than done. Many such efforts are stymied from the outset, as privacy concerns make it difficult for scientists to access the data they would like to work with.
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