It is critical that any data preparation performed on a training dataset is also performed on a new dataset in the future. This may include a test dataset when evaluating a model or new data from the domain when using a model to make predictions. Typically, the model fit on the training dataset is saved […]
Deep learning neural networks learn how to map inputs to outputs from examples in a training dataset. The weights of the model are initialized to small random values and updated via an optimization algorithm in response to estimates of error on the training dataset. Given the use of small weights in the model and the […]
The vanishing gradients problem is one example of unstable behavior that you may encounter when training a deep neural network. It describes the situation where a deep multilayer feed-forward network or a recurrent neural network is unable to propagate useful gradient information from the output end of the model back to the layers near the […]
Given the rise of smart electricity meters and the wide adoption of electricity generation technology like solar panels, there is a wealth of electricity usage data available. This data represents a multivariate time series of power-related variables that in turn could be used to model and even forecast future electricity consumption. Machine learning algorithms predict […]
Spot-checking algorithms is a technique in applied machine learning designed to quickly and objectively provide a first set of results on a new predictive modeling problem. Unlike grid searching and other types of algorithm tuning that seek the optimal algorithm or optimal configuration for an algorithm, spot-checking is intended to evaluate a diverse set of […]
Human activity recognition is the problem of classifying sequences of accelerometer data recorded by specialized harnesses or smart phones into known well-defined movements. It is a challenging problem given the large number of observations produced each second, the temporal nature of the observations, and the lack of a clear way to relate accelerometer data to […]
Time series data often requires some preparation prior to being modeled with machine learning algorithms. For example, differencing operations can be used to remove trend and seasonal structure from the sequence in order to simplify the prediction problem. Some algorithms, such as neural networks, prefer data to be standardized and/or normalized prior to modeling. Any […]
A What is the difference between “standardization” and “normalization”? Standardization refers to scaling a variable that has a Gaussian distribution such that it has a mean of zero and a standard deviation of one. Normalization refers to scaling a variable that has any distribution so that all values are between zero and one. It is […]
A When should I standardize and normalize data? Standardization refers to scaling a variable that has a Gaussian distribution such that it has a mean of zero and a standard deviation of one. Normalization refers to scaling a variable that has any distribution so that all values are between zero and one. It is possible […]
Neural networks like Long Short-Term Memory (LSTM) recurrent neural networks are able to almost seamlessly model problems with multiple input variables. This is a great benefit in time series forecasting, where classical linear methods can be difficult to adapt to multivariate or multiple input forecasting problems. In this tutorial, you will discover how you can […]
