https://github.com/lord3008/working-with-data-augmentation

This repository showcases my work in Data Augmentation 📈⭐. It's a method used when there is a shortage of data 📉⭐. It helps us in handling data deficiency, ensuring better model performance and robustness 🚀⭐.
https://github.com/lord3008/working-with-data-augmentation

Last synced: 4 days ago
JSON representation

This repository showcases my work in Data Augmentation 📈⭐. It's a method used when there is a shortage of data 📉⭐. It helps us in handling data deficiency, ensuring better model performance and robustness 🚀⭐.

• Host: GitHub
• URL: https://github.com/lord3008/working-with-data-augmentation
• Owner: Lord3008
• Created: 2024-06-21T05:23:19.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-06-22T14:16:07.000Z (5 months ago)
• Last Synced: 2024-06-22T22:18:53.740Z (5 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 239 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Data Augmentation 📈⭐

**Data Augmentation** is a powerful technique used in machine learning and deep learning to artificially increase the size of a training dataset. It involves creating new data points from the existing data by applying various transformations. This helps to improve the performance and robustness of models, especially when there is a shortage of data 📉⭐. Here's an in-depth look at data augmentation, its types, and various use cases.

### Why Use Data Augmentation?

- **Handles Data Deficiency**: When collecting large datasets is challenging or expensive, data augmentation provides a way to generate additional training examples.

- **Improves Model Performance**: By exposing the model to a variety of augmented data, it learns to generalize better, reducing overfitting.

- **Increases Robustness**: Models become more robust to variations and distortions in the data, leading to better performance on real-world data.

### Types of Data Augmentation ⭐

1. **Image Data Augmentation**:

   - **Flipping**: Horizontal and vertical flips.

   - **Rotation**: Rotating images by a certain degree.

   - **Scaling**: Zooming in and out.

   - **Translation**: Shifting images horizontally or vertically.

   - **Cropping**: Randomly cropping parts of the image.

   - **Color Jittering**: Changing brightness, contrast, saturation, and hue.

   - **Adding Noise**: Introducing random noise to images.

   - **Cutout**: Removing a part of the image by masking a random square area.

2. **Text Data Augmentation**:

   - **Synonym Replacement**: Replacing words with their synonyms.

   - **Random Insertion**: Inserting random words from the vocabulary.

   - **Random Swap**: Swapping words in a sentence randomly.

   - **Random Deletion**: Deleting words randomly.

   - **Back Translation**: Translating a sentence to another language and back to introduce variation.

3. **Audio Data Augmentation**:

   - **Time Stretching**: Speeding up or slowing down the audio.

   - **Pitch Shifting**: Changing the pitch of the audio.

   - **Adding Noise**: Introducing background noise.

   - **Shifting**: Moving the audio signal in time.

   - **Volume Control**: Increasing or decreasing the volume.

4. **Tabular Data Augmentation**:

   - **Adding Noise**: Adding slight variations to numerical features.

   - **SMOTE (Synthetic Minority Over-sampling Technique)**: Creating synthetic samples for imbalanced datasets.

   - **Mixup**: Creating new samples by mixing pairs of examples.

### Use Cases of Data Augmentation ⭐

1. **Image Classification**:

   - Augmenting training data to improve the performance of image classifiers in applications like object detection, face recognition, and medical imaging.

2. **Natural Language Processing (NLP)**:

   - Enhancing datasets for tasks like sentiment analysis, language translation, and text classification.

3. **Speech Recognition**:

   - Improving the robustness of speech recognition systems by augmenting audio data.

4. **Time Series Forecasting**:

   - Augmenting time series data to improve the performance of models in predicting stock prices, weather conditions, and other temporal patterns.

5. **Medical Imaging**:

   - Generating more examples of medical images to improve the performance of models in detecting diseases from X-rays, MRIs, and CT scans.

6. **Autonomous Driving**:

   - Augmenting visual data from cameras to train models for self-driving cars, enhancing their ability to recognize objects and navigate safely.

By leveraging data augmentation, we can effectively tackle data limitations, enhance model performance, and create more resilient machine learning applications ⭐🚀.