A language model can predict the probability of the next word in the sequence, based on the words already observed in the sequence. Neural network models are a preferred method for developing statistical language models because they can use a distributed representation where different words with similar meanings have similar representation and because they can […]
Natural Language Processing, or NLP for short, is broadly defined as the automatic manipulation of natural language, like speech and text, by software. The study of natural language processing has been around for more than 50 years and grew out of the field of linguistics with the rise of computers. In this post, you will […]
Natural Language Processing, or NLP, is a subfield of machine learning concerned with understanding speech and text data. Statistical methods and statistical machine learning dominate the field and more recently deep learning methods have proven very effective in challenging NLP problems like speech recognition and text translation. In this post, you will discover the Stanford […]
Recurrent neural networks, or RNNs, are a type of artificial neural network that add additional weights to the network to create cycles in the network graph in an effort to maintain an internal state. The promise of adding state to neural networks is that they will be able to explicitly learn and exploit context in […]
Once you choose a machine learning algorithm for your classification problem, you need to report the performance of the model to stakeholders. This is important so that you can set the expectations for the model on new data. A common mistake is to report the classification accuracy of the model alone. In this post, you […]
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 […]
Time series forecasting is a process, and the only way to get good forecasts is to practice this process. In this tutorial, you will discover how to forecast the monthly sales of French champagne with Python. Working through this tutorial will provide you with a framework for the steps and the tools for working through […]
Time series forecasting is a process, and the only way to get good forecasts is to practice this process. In this tutorial, you will discover how to forecast the annual water usage in Baltimore with Python. Working through this tutorial will provide you with a framework for the steps and the tools for working through […]
Time series forecasting is a process, and the only way to get good forecasts is to practice this process. In this tutorial, you will discover how to forecast the number of monthly armed robberies in Boston with Python. Working through this tutorial will provide you with a framework for the steps and the tools for […]
Time series is different from more traditional classification and regression predictive modeling problems. The temporal structure adds an order to the observations. This imposed order means that important assumptions about the consistency of those observations needs to be handled specifically. For example, when modeling, there are assumptions that the summary statistics of observations are consistent. […]
