Weight regularization is a technique used in deep learning to prevent overfitting, which occurs when a model is too complex and is able to memorize the training data instead of learning from it. There are two common types of weight regularization: L1 regularization and L2 regularization.

L1 regularization adds a penalty term to the model’s cost function that is proportional to the absolute value of the weights. L2 regularization, on the other hand, adds a penalty term that is proportional to the sum of the squares of the weights.

In some cases, it can be beneficial to use both L1 and L2 regularization together, which is known as L1/L2 regularization. This can help to balance the strengths and weaknesses of both L1 and L2 regularization, resulting in a more robust model.

In Keras, a deep learning library for Python, adding L1/L2 regularization to a model is quite easy. First, you will need to import the necessary modules from Keras. Next, you will need to create your model using the Sequential class, which allows you to add layers to your model one by one.

After creating your model, you will need to add a dense layer, which is a type of layer that is fully connected to the previous layer. You can specify the number of neurons in this layer and the activation function to use. The activation function is a mathematical function that is applied to the output of the layer to introduce non-linearity into the model.

To add L1/L2 regularization to your model, you will need to create two instances of the regularizers class and specify the values of the L1 and L2 regularization parameters, also known as the lambda parameters. These values determine how much the model will be penalized for having large weights.

Finally, you will need to compile your model by specifying the optimizer, loss function, and metrics to use. The optimizer is the algorithm used to update the weights of the model based on the cost function. The loss function is the function that measures the difference between the predicted and actual values. The metrics are used to evaluate the performance of the model.

In summary, adding L1/L2 regularization to a deep learning model in Keras is a simple process that involves importing the necessary modules, creating the model, adding a dense layer, creating two instances of the regularizers class, and specifying the L1 and L2 regularization parameters. Finally, the model needs to be compiled by specifying the optimizer, loss function, and metrics.

In this Applied Machine Learning & Data Science Recipe, the reader will find the practical use of applied machine learning and data science in Python & R programming: Learn By Example | How to use l1_l2 regularization to a Deep Learning Model in Keras?

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause.The information presented here could also be found in public knowledge domains.

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