The Keras Python library for deep learning focuses on creating models as a sequence of layers. In this post, you will discover the simple components you can use to create neural networks and simple deep learning models using Keras from TensorFlow. Let’s get started. May 2016: First version Update Mar/2017: Updated example for Keras 2.0.2, […]
Keras is a simple and powerful Python library for deep learning. Since deep learning models can take hours, days, and even weeks to train, it is important to know how to save and load them from a disk. In this post, you will discover how to save your Keras models to files and load them […]
Keras is a powerful and easy-to-use free open source Python library for developing and evaluating deep learning models. It is part of the TensorFlow library and allows you to define and train neural network models in just a few lines of code. In this tutorial, you will discover how to create your first deep learning neural […]
You’ve probably been told to standardize or normalize inputs to your model to improve performance. But what is normalization and how can we implement it easily in our deep learning models to improve performance? Normalizing our inputs aims to create a set of features that are on the same scale as each other, which we’ll […]
TensorFlow is a Python library for fast numerical computing created and released by Google. It is a foundation library that can be used to create Deep Learning models directly or by using wrapper libraries that simplify the process built on top of TensorFlow. In this post, you will discover the TensorFlow library for Deep Learning. […]
It can be challenging to develop a neural network predictive model for a new dataset. One approach is to first inspect the dataset and develop ideas for what models might work, then explore the learning dynamics of simple models on the dataset, then finally develop and tune a model for the dataset with a robust […]
Some prediction problems require predicting both numeric values and a class label for the same input. A simple approach is to develop both regression and classification predictive models on the same data and use the models sequentially. An alternative and often more effective approach is to develop a single neural network model that can predict […]
It can be challenging to develop a neural network predictive model for a new dataset. One approach is to first inspect the dataset and develop ideas for what models might work, then explore the learning dynamics of simple models on the dataset, then finally develop and tune a model for the dataset with a robust […]
It can be challenging to develop a neural network predictive model for a new dataset. One approach is to first inspect the dataset and develop ideas for what models might work, then explore the learning dynamics of simple models on the dataset, then finally develop and tune a model for the dataset with a robust […]
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 […]
