Deep learning neural network models used for predictive modeling may need to be updated. This may be because the data has changed since the model was developed and deployed, or it may be the case that additional labeled data has been made available since the model was developed and it is expected that the additional data will improve the performance of the model. It is important to experiment and evaluate with a range of different approaches when updating neural network models for new data, especially if model updating will be automated, such as on a periodic schedule. There are many ways to update neural network models, although the two main approaches involve either using the existing model as a starting point and retraining it, or leaving the existing model unchanged and combining the predictions from the existing model with a new model. In this tutorial, you will discover how to update deep learning neural network models in response to new data.

Weight constraints provide an approach to reduce the overfitting of a deep learning neural network model on the training data and improve the performance of the model on new data, such as the holdout test set. There are multiple types of weight constraints, such as maximum and unit vector norms, and some require a hyperparameter that must be configured. In this tutorial, you will discover the Keras API for adding weight constraints to deep learning neural network models to reduce overfitting. How to Reduce Overfitting in Deep Neural Networks With Weight Constraints in Keras Photo by Ian Sane, some rights reserved. The Keras API supports weight constraints.

The summary can be created by calling the summary() function on the model that returns a string that in turn can be printed. Below is the updated example that prints a summary of the created model. Running this example prints the following table. We can clearly see the output shape and number of weights in each layer. The summary is useful for simple models, but can be confusing for models that have multiple inputs or outputs. Keras also provides a function to create a plot of the network neural network graph that can make more complex models easier to understand. The plot_model() function in Keras will create a plot of your network.

Not only can Amazon predict what products you're most likely to purchase, but it is also has the ability to guess your age. The firm's AI, Rekognition, received an update that provides an estimated age range of a person in an uploaded image – the value is expressed in years and is returned as a pair of integers. Amazon believes this new attribute can be used to power public safety applications, collect demographics or create a timelapse in photos. The firm's AI, Rekognition, received an update that provides an estimated age range of a person in an uploaded image. Amazon Rekognition is a developer toolkit that is part of the firm's AWS cloud computing service.