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Deep Learning - CNN - Convolutional Neural Network - Data Augmentation Tutorial

It is a simple technique using which we reduce overfitting. In data augmentation, suppose we are working on a dataset, where we have limited data and deep learning requires more data, so in this case we can generate data using data augmentation.

For eg. if we have one photo, using the Keras image generator we can create a new photo. This process is known as data augmentation which will help in reducing overfitting.

For more - https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html

This is what our data augmentation strategy looks like

 

 

Data Augmentation Practical Link

Final Result - 

Deep Learning

Deep Learning

  • Introduction
  • LSTM - Long Short Term Memory
    • Introduction
  • ANN - Artificial Neural Network
    • Perceptron
    • Multilayer Perceptron (Notation & Memoization)
    • Forward Propagation
    • Backward Propagation
    • Perceptron Loss Function
    • Loss Function
    • Gradient Descent | Batch, Stochastics, Mini Batch
    • Vanishing & Exploding Gradient Problem
    • Early Stopping, Dropout. Weight Decay
    • Data Scaling & Feature Scaling
    • Regularization
    • Activation Function
    • Weight Initialization Techniques
    • Optimizer
    • Keras Tuner | Hyperparameter Tuning
  • CNN - Convolutional Neural Network
    • Introduction
    • Padding & Strides
    • Pooling Layer
    • CNN Architecture
    • Backpropagation in CNN
    • Data Augmentation
    • Pretrained Model & Transfer Learning
    • Keras Functional Model
  • RNN - Recurrent Neural Network
    • RNN Architecture & Forward Propagation
    • Types Of RNN
    • Backpropagation in RNN
    • Problems with RNN

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