Create more training data.

What is Data Augmentation?

Creating new training examples from existing ones.

**Goal**: More data → Better model

Image Augmentation

```python from tensorflow.keras.preprocessing.image import ImageDataGenerator

Define augmentations datagen = ImageDataGenerator( rotation_range=20, # Rotate up to 20 degrees width_shift_range=0.2, # Shift left/right height_shift_range=0.2, # Shift up/down horizontal_flip=True, # Mirror image zoom_range=0.2, # Zoom in/out fill_mode='nearest' )

Apply to images augmented_images = datagen.flow(images, batch_size=32)

Use in training model.fit(augmented_images, epochs=50) ```

Albumentations (Better for Images)

```python import albumentations as A import cv2

Define pipeline transform = A.Compose([ A.RandomRotate90(), A.Flip(), A.Transpose(), A.GaussNoise(), A.OneOf([ A.MotionBlur(p=0.2), A.MedianBlur(blur_limit=3, p=0.1), A.Blur(blur_limit=3, p=0.1), ], p=0.2), A.RandomBrightnessContrast(p=0.2), ])

Apply image = cv2.imread('photo.jpg') augmented = transform(image=image)['image'] ```

Text Augmentation

```python import nlpaug.augmenter.word as naw

Synonym replacement aug = naw.SynonymAug(aug_src='wordnet') text = "The food in Miami is amazing" augmented = aug.augment(text) print(augmented) # "The food in Miami is fantastic"

Back translation (more realistic) aug = naw.BackTranslationAug( from_model_name='facebook/wmt19-en-de', to_model_name='facebook/wmt19-de-en' ) augmented = aug.augment(text) ```

Audio Augmentation

```python import librosa import numpy as np

def augment_audio(audio, sr): # Time stretch audio_stretched = librosa.effects.time_stretch(audio, rate=1.1) # Pitch shift audio_pitched = librosa.effects.pitch_shift(audio, sr=sr, n_steps=2) # Add noise noise = np.random.randn(len(audio)) audio_noisy = audio + 0.005 * noise return [audio_stretched, audio_pitched, audio_noisy] ```

Tabular Data Augmentation

```python from sklearn.utils import resample import pandas as pd

Oversample minority class df_minority = df[df['target'] == 1] df_majority = df[df['target'] == 0]

Resample with replacement df_minority_upsampled = resample( df_minority, replace=True, n_samples=len(df_majority), random_state=42 )

Combine df_balanced = pd.concat([df_majority, df_minority_upsampled]) ```

SMOTE for Tabular Data

```python from imblearn.over_sampling import SMOTE

Create synthetic samples smote = SMOTE() X_resampled, y_resampled = smote.fit_resample(X, y)

print(f"Original: {len(X)}") print(f"After SMOTE: {len(X_resampled)}") ```

MixUp (Advanced)

Mix two images:

```python def mixup(x1, y1, x2, y2, alpha=0.2): # Random mixing ratio lam = np.random.beta(alpha, alpha) # Mix images x = lam * x1 + (1 - lam) * x2 # Mix labels y = lam * y1 + (1 - lam) * y2 return x, y

Use in training x_mix, y_mix = mixup(image1, label1, image2, label2) ```

CutMix

Cut part of image and paste another:

```python def cutmix(x1, y1, x2, y2, alpha=1.0): lam = np.random.beta(alpha, alpha) # Random box h, w = x1.shape[:2] cut_h = int(h * np.sqrt(1 - lam)) cut_w = int(w * np.sqrt(1 - lam)) cx = np.random.randint(w) cy = np.random.randint(h) x1y1 = np.clip([cx - cut_w // 2, cy - cut_h // 2], 0, [w, h]) x2y2 = np.clip([cx + cut_w // 2, cy + cut_h // 2], 0, [w, h]) # Replace region x1[x1y1[1]:x2y2[1], x1y1[0]:x2y2[0]] = x2[x1y1[1]:x2y2[1], x1y1[0]:x2y2[0]] return x1, y1 ```

Best Practices

1. **Images**: Always augment (flip, rotate, crop) 2. **Text**: Be careful (meaning shouldn't change) 3. **Validation**: Never augment validation data! 4. **Realistic**: Augmentations should be realistic

Remember

- More data = better model - Augment during training - Don't overdo it - Keep validation data clean