Your Definitive Guide to Data Labeling
for Machine Learning and Deep Learning Projects
The key to the successful implementation of most AI tasks is the availability of a sufficiently large, labeled data set with which the AI models can be trained upon. The strength of an AI system depends on the robustness of the AI algorithms along with the quality and quantity of training data. This is where Data Labeling becomes the backbone of computer vision-based machine learning and deep learning projects.
Our eBook acts as a comprehensive handbook to Data Labeling requirements like the essential elements of this vital but time-consuming task, a few best practices to label the data, and what to look for when choosing the right data labeling platform.
Key takeaways from this eBook
- Wide understanding of data labeling and the various types of labeling
- Factors/components that influence the labeling process
- How active learning can help improve the accuracy of data labeling
- Choosing the right data labeling platform