data augmentation

data-augmentation

Data augmentation is a powerful technique used in machine learning, particularly in image classification tasks. It involves creating additional training data by applying various transformations to existing data, such as rotations, flips, or color adjustments. This process helps improve the performance of models by providing them with more diverse examples, allowing them to generalize better to unseen data. By teaching classifiers to recognize objects despite variations in appearance, data augmentation enhances their robustness and accuracy. Ultimately, it is a cost-effective way to increase the size of training datasets without the need for collecting new data.

TensorFlow Tutorials

Overview This tutorial demonstrates data augmentation: a technique to increase the diversity of your training set by applying random (but realistic) transformations, such as image rotation. You will l...

Data Augmentation

Kaggle Learn Courses

<!--TITLE:Data Augmentation-- Introduction Now that you've learned the fundamentals of convolutional classifiers, you're ready to move on to more advanced topics. In this lesson, you'll learn a trick ...

Data Augmentation

Kaggle Learn Courses

Data Augmentation

Kaggle Learn Courses

Maximizing the Impact of Data Augmentation: Effective Techniques and Best Practices

Towards AI

Data augmentation is a popular technique in machine learning that involves creating new data from existing data by making various modifications to it. These modifications could include adding noise…

Text Data Augmentation

Towards Data Science

Data Augmentation is the process that enables us to increase the size of the training data without actually collecting the data. But why do we need more data? The answer is simple — the more data we…

A Short Survey on Implicit Data Augmentation

Towards Data Science

Data augmentation is a popular technique used to increase the generalizability of a possibly overfitting model. By generating additional training data and exposing the model to different versions of…

Data augmentations in fastai

Towards Data Science

Data augmentation refers to randomly applying various kinds of transforms to the images in our dataset. These transforms help introduce more variety in our dataset. Let’s see what that means…

Exploring Image Data Augmentation with Keras and Tensorflow

Towards Data Science

Data augmentation is a strategy used to increase the amount of data by using techniques like cropping, padding, flipping, etc. Data augmentation makes the model more robust to slight variations, and…

Data Augmentation: What, Why and How……

Analytics Vidhya

Data augmentation is a regularization technique like Lasso, Hinge, L1, and L2 regularizations, it also participates with great help during any machine learning model training process. In general…

Balancing the Regularization Effect of Data Augmentation

Towards Data Science

When training neural networks, data augmentation is one of the most commonly used pre-processing techniques. The word “augmentation” which literally means “the action or process of making or becoming…...

Data Augmentation Techniques using OpenCV

Analytics Vidhya

Data augmentation is a strategy that enables practitioners to significantly increase the diversity of data available for training models, without actually collecting new data. Data augmentation…