- 6 Mistakes To Avoid While Training Your Machine Learning Model - Apr 15, 2021.
While training the AI model, multi-stage activities are performed to utilize the training data in the best manner, so that outcomes are satisfying. So, here are the 6 common mistakes you need to understand to make sure your AI model is successful.
- How Noisy Labels Impact Machine Learning Models - Apr 6, 2021.
Not all training data labeling errors have the same impact on the performance of the Machine Learning system. The structure of the labeling errors make a difference. Read iMerit’s latest blog to learn how to minimize the impact of labeling errors.
- Introduction to Federated Learning - Aug 20, 2020.
Federated learning means enabling on-device training, model personalization, and more. Read more about it in this article.
- Labelling Data Using Snorkel - Jul 24, 2020.
In this tutorial, we walk through the process of using Snorkel to generate labels for an unlabelled dataset. We will provide you examples of basic Snorkel components by guiding you through a real clinical application of Snorkel.
- Achieving Accuracy with your Training Dataset - Mar 5, 2020.
How do we make sure our training data is more accurate than the rest? Partners like Supahands eliminate the headache that comes with labeling work by providing end-to-end managed labeling solutions, completed by a fully managed workforce that is trained to work on your model specifics.
- Hand labeling is the past. The future is #NoLabel AI - Feb 19, 2020.
Data labeling is so hot right now… but could this rapidly emerging market face disruption from a small team at Stanford and the Snorkel open source project, which enables highly efficient programmatic labeling that is 10 to 1,000x as efficient as hand labeling?
- The Rise of User-Generated Data Labeling - Dec 4, 2019.
Let’s say your project is humongous and needs data labeling to be done continuously - while you’re on-the-go, sleeping, or eating. I’m sure you’d appreciate User-generated Data Labeling. I’ve got 6 interesting examples to help you understand this, let’s dive right in!
- How Data Labeling Facilitates AI Models - Oct 31, 2019.
AI-based models are highly dependent on accurate, clean, well-labeled, and prepared data in order to produce the desired output and cognition. These models are fed with bulky datasets covering an array of probabilities and computations to make its functioning as smart and gifted as human intelligence.
- High-Quality AI And Machine Learning Data Labeling At Scale: A Brief Research Report - Jul 25, 2019.
Analyst firm Cognilytica estimates that as much as 80% of machine learning project time is spent on aggregating, cleaning, labeling, and augmenting machine learning model data. So, how do innovative machine learning teams prepare data in such a way that they can trust its quality, cost of preparation, and the speed with which it’s delivered?
- Crowdsourcing vs. Managed Teams: A Study in Data Labeling Quality - Jun 12, 2019.
You need data labeled for ML. You can do it in-house, crowdsource it, or hire a managed service. If data quality matters, read this.
- How to Organize Data Labeling for Machine Learning: Approaches and Tools - May 16, 2018.
The main challenge for a data science team is to decide who will be responsible for labeling, estimate how much time it will take, and what tools are better to use.
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