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 […]
Grid searching is generally not an operation that we can perform with deep learning methods. This is because deep learning methods often require large amounts of data and large models, together resulting in models that take hours, days, or weeks to train. In those cases where the datasets are smaller, such as univariate time series, […]
Long Short-Term Memory networks, or LSTMs for short, can be applied to time series forecasting. There are many types of LSTM models that can be used for each specific type of time series forecasting problem. In this tutorial, you will discover how to develop a suite of LSTM models for a range of standard time […]
Time series forecasting with LSTMs directly has shown little success. This is surprising as neural networks are known to be able to learn complex non-linear relationships and the LSTM is perhaps the most successful type of recurrent neural network that is capable of directly supporting multivariate sequence prediction problems. A recent study performed at Uber […]
A When should I use an MLP, CNN and RNN? A Multilayer Perceptron or MLP can approximate a mapping function from inputs to outputs. They are flexible and can be adapted to most problems, nevertheless, they are perhaps more suited to classification and regression problems. A Convolutional Neural Network or CNN was developed and is […]
Caption generation is a challenging artificial intelligence problem that draws on both computer vision and natural language processing. The encoder-decoder recurrent neural network architecture has been shown to be effective at this problem. The implementation of this architecture can be distilled into inject and merge based models, and both make different assumptions about the role […]
Text summarization is a problem in natural language processing of creating a short, accurate, and fluent summary of a source document. The Encoder-Decoder recurrent neural network architecture developed for machine translation has proven effective when applied to the problem of text summarization. It can be difficult to apply this architecture in the Keras deep learning […]
Text summarization is the task of creating short, accurate, and fluent summaries from larger text documents. Recently deep learning methods have proven effective at the abstractive approach to text summarization. In this post, you will discover three different models that build on top of the effective Encoder-Decoder architecture developed for sequence-to-sequence prediction in machine translation. […]
Caption generation is a challenging artificial intelligence problem where a textual description must be generated for a photograph. It requires both methods from computer vision to understand the content of the image and a language model from the field of natural language processing to turn the understanding of the image into words in the right […]
Captioning an image involves generating a human readable textual description given an image, such as a photograph. It is an easy problem for a human, but very challenging for a machine as it involves both understanding the content of an image and how to translate this understanding into natural language. Recently, deep learning methods have […]
