Keras: 100 Tips and Techniques for Efficient Deep Learning Model Deployment

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Here are 100 tips for working with Keras, a high-level neural networks API written in Python and capable of running on top of TensorFlow, Theano, or Microsoft Cognitive Toolkit:

1. Basics of Keras:

1. Import Keras with import keras.
2. Install Keras using pip install keras.
3. Use keras.Sequential() to create a linear stack of layers.
4. Add layers to a model using model.add(layer).
5. Visualize the model architecture with model.summary().
6. Compile a model with model.compile(optimizer, loss, metrics).
7. Use model.fit() to train the model.
8. Evaluate the model with model.evaluate().
9. Predict with a trained model using model.predict().

2. Layers and Models:

1. Use dense layers with keras.layers.Dense(units, activation).
2. Add dropout to prevent overfitting with keras.layers.Dropout(rate).
3. Use activation functions like ‘relu’, ‘sigmoid’, or ‘softmax’.
4. Create a sequential model for a linear stack of layers.
5. Use functional API for complex…