user@devops:~$ cat README.md
Regularization & Callbacks
# Description
Comprehensive study of Keras regularization techniques and callbacks to improve deep learning model generalization. Implements L1 and L2 regularization on dense and convolutional layers, dropout with varying rates, and a full suite of Keras callbacks: EarlyStopping to halt training when validation stops improving, ModelCheckpoint to save the best model, ReduceLROnPlateau to reduce learning rate on plateaus, TensorBoard for real-time metric visualization, and CSVLogger for historical logging. Each technique is evaluated individually and in combination, comparing learning curves and final accuracy on an image classification problem.
# Key features
$ L1, L2 and L1_L2 (elastic net) regularization on Keras layers
$ Dropout with varying rates and positions in the network
$ EarlyStopping with patience and best weight restoration
$ ModelCheckpoint to save best model during training
$ ReduceLROnPlateau for adaptive learning rate adjustment
$ TensorBoard for real-time metrics and graph visualization
$ CSVLogger for training history recording and analysis
# Gallery
# Technologies used
