Data augmentation
Improve Your NLP Solutions with Data Augmentation in 2024
Introduction Natural Language Processing (NLP) offers a plethora of use cases that your business can benefit. As a cutting-edge research field, methods to improve NLP models and use cases are constantly being developed.
Data Augmentation to Improve Deep Learning Models in 2024
Performance and accuracy level of deep learning models depend on the volume and diversity of training data which is used to feed and train neural network architectures. Therefore, challenges in collecting and labelling of training data can limit development of deep learning models.
Top Data Augmentation Techniques: Ultimate Guide for 2024
Data augmentation techniques generate different versions of a real dataset artificially to increase its size. Computer vision and natural language processing (NLP) models use data augmentation strategy to handle with data scarcity and insufficient data diversity. Data-centric AI/ML development practices such as data augmentation can increase accuracy of machine learning models.
What is Data Augmentation? Techniques & Examples in 2024
The performance of most ML models, and deep learning models in particular, depends on the quality, quantity and relevancy of training data. However, insufficient data is one of the most common challenges in implementing machine learning in the enterprise. This is because collecting such data can be costly and time-consuming in many cases.