Category: Machine Learning

Articles About Machine Learning

Machine Learning

Dropout with LSTM Networks for Time Series Forecasting

Last Updated on August 28, 2020 Long Short-Term Memory (LSTM) models are a type of recurrent neural network capable of learning sequences of observations. This may make them a network well suited to time series forecasting. An issue with LSTMs is that they can easily overfit training data, reducing their predictive skill. Dropout is a regularization method where input and recurrent connections to LSTM units are probabilistically excluded from activation and weight updates while training a network. This has the […]

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Machine Learning

Estimate the Number of Experiment Repeats for Stochastic Machine Learning Algorithms

Last Updated on August 14, 2020 A problem with many stochastic machine learning algorithms is that different runs of the same algorithm on the same data return different results. This means that when performing experiments to configure a stochastic algorithm or compare algorithms, you must collect multiple results and use the average performance to summarize the skill of the model. This raises the question as to how many repeats of an experiment are enough to sufficiently characterize the skill of […]

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Machine Learning

How to Use Statistical Significance Tests to Interpret Machine Learning Results

Last Updated on August 8, 2019 It is good practice to gather a population of results when comparing two different machine learning algorithms or when comparing the same algorithm with different configurations. Repeating each experimental run 30 or more times gives you a population of results from which you can calculate the mean expected performance, given the stochastic nature of most machine learning algorithms. If the mean expected performance from two algorithms or configurations are different, how do you know […]

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Machine Learning

Weight Regularization with LSTM Networks for Time Series Forecasting

Last Updated on August 28, 2020 Long Short-Term Memory (LSTM) models are a recurrent neural network capable of learning sequences of observations. This may make them a network well suited to time series forecasting. An issue with LSTMs is that they can easily overfit training data, reducing their predictive skill. Weight regularization is a technique for imposing constraints (such as L1 or L2) on the weights within LSTM nodes. This has the effect of reducing overfitting and improving model performance. […]

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Machine Learning

How to Convert a Time Series to a Supervised Learning Problem in Python

Last Updated on August 21, 2019 Machine learning methods like deep learning can be used for time series forecasting. Before machine learning can be used, time series forecasting problems must be re-framed as supervised learning problems. From a sequence to pairs of input and output sequences. In this tutorial, you will discover how to transform univariate and multivariate time series forecasting problems into supervised learning problems for use with machine learning algorithms. After completing this tutorial, you will know: How […]

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Machine Learning

Multistep Time Series Forecasting with LSTMs in Python

Last Updated on August 28, 2020 The Long Short-Term Memory network or LSTM is a recurrent neural network that can learn and forecast long sequences. A benefit of LSTMs in addition to learning long sequences is that they can learn to make a one-shot multi-step forecast which may be useful for time series forecasting. A difficulty with LSTMs is that they can be tricky to configure and it can require a lot of preparation to get the data in the […]

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Machine Learning

Demonstration of Memory with a Long Short-Term Memory Network in Python

Last Updated on August 27, 2020 Long Short-Term Memory (LSTM) networks are a type of recurrent neural network capable of learning over long sequences. This differentiates them from regular multilayer neural networks that do not have memory and can only learn a mapping between input and output patterns. It is important to understand the capabilities of complex neural networks like LSTMs on small contrived problems as this understanding will help you scale the network up to large and even very […]

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Machine Learning

How to use Different Batch Sizes when Training and Predicting with LSTMs

Last Updated on August 14, 2019 Keras uses fast symbolic mathematical libraries as a backend, such as TensorFlow and Theano. A downside of using these libraries is that the shape and size of your data must be defined once up front and held constant regardless of whether you are training your network or making predictions. On sequence prediction problems, it may be desirable to use a large batch size when training the network and a batch size of 1 when […]

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Machine Learning

How to Use the TimeDistributed Layer in Keras

Last Updated on August 14, 2019 Long Short-Term Networks or LSTMs are a popular and powerful type of Recurrent Neural Network, or RNN. They can be quite difficult to configure and apply to arbitrary sequence prediction problems, even with well defined and “easy to use” interfaces like those provided in the Keras deep learning library in Python. One reason for this difficulty in Keras is the use of the TimeDistributed wrapper layer and the need for some LSTM layers to […]

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Machine Learning

Learn to Add Numbers with an Encoder-Decoder LSTM Recurrent Neural Network

Last Updated on August 27, 2020 Long Short-Term Memory (LSTM) networks are a type of Recurrent Neural Network (RNN) that are capable of learning the relationships between elements in an input sequence. A good demonstration of LSTMs is to learn how to combine multiple terms together using a mathematical operation like a sum and outputting the result of the calculation. A common mistake made by beginners is to simply learn the mapping function from input term to the output term. […]

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