- How our Obsession with Algorithms Broke Computer Vision: And how Synthetic Computer Vision can fix it - Oct 15, 2021.
Deep Learning radically improved Machine Learning as a whole. The Data-Centric revolution is about to do the same. In this post, we’ll take a look at the pitfalls of mainstream Computer Vision (CV) and discuss why Synthetic Computer Vision (SCV) is the future.
- Create Synthetic Time-series with Anomaly Signatures in Python - Oct 12, 2021.
A simple and intuitive way to create synthetic (artificial) time-series data with customized anomalies — particularly suited to industrial applications.
- Teaching AI to Classify Time-series Patterns with Synthetic Data - Oct 1, 2021.
How to build and train an AI model to identify various common anomaly patterns in time-series data.
- Build a synthetic data pipeline using Gretel and Apache Airflow - Sep 2, 2021.
In this blog post, we build an ETL pipeline that generates synthetic data from a PostgreSQL database using Gretel’s Synthetic Data APIs and Apache Airflow.
- 3 Data Acquisition, Annotation, and Augmentation Tools - Aug 27, 2021.
Check out these 3 projects found around GitHub that can help with your data acquisition, annotation, and augmentation tasks.
- An overview of synthetic data types and generation methods - Feb 22, 2021.
Synthetic data can be used to test new products and services, validate models, or test performances because it mimics the statistical property of production data. Today you'll find different types of structured and unstructured synthetic data.
- 10 Use Cases for Privacy-Preserving Synthetic Data - Aug 11, 2020.
This article presents 10 use-cases for synthetic data, showing how enterprises today can use this artificially generated information to train machine learning models or share data externally without violating individuals' privacy.
- Scikit-Learn & More for Synthetic Dataset Generation for Machine Learning - Sep 19, 2019.
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.
- 5 Ways to Deal with the Lack of Data in Machine Learning - Jun 10, 2019.
Effective solutions exist when you don't have enough data for your models. While there is no perfect approach, five proven ways will get your model to production.
- Synthetic Data Generation: A must-have skill for new data scientists - Dec 27, 2018.
A brief rundown of methods/packages/ideas to generate synthetic data for self-driven data science projects and deep diving into machine learning methods.
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