Author: Awais Farooq
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Monitor Performance
Use TensorBoard with Keras for real-time visualization of metrics (loss, accuracy) and model graphs. It helps in understanding and optimizing model performance.
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Transfer Learning
Leverage pre-trained models (like those from keras.applications) for tasks with limited data. Fine-tuning these models on your specific dataset can yield better results than training from scratch.
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Regularization Techniques
Incorporate dropout, L1/L2 regularization, or batch normalization to prevent overfitting and improve generalization.
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Utilize Callbacks for Monitoring
Keras callbacks (e.g., EarlyStopping, ModelCheckpoint) are powerful for automating tasks during training, such as saving the best model, stopping training early upon convergence, or adjusting learning rates dynamically.
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Use the Functional API for Flexibility
While Sequential models are straightforward, the Functional API allows for more complex models, including multi-input and multi-output models. It’s essential for building custom architectures.
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Choose the Right Backend
Keras supports multiple backends like TensorFlow, Theano, and CNTK. TensorFlow is the default and most widely used. Choose the backend that best suits your needs and hardware.