Managing Machine Learning Workflows with Scikit-learn Pipelines Part 3: Multiple Models, Pipelines, and Grid Searches

[unable to retrieve full-text content]In this post, we will be using grid search to optimize models built from a number of different types estimators, which we will then compare and properly evaluate the best hyperparameters that each model has to offer.
Original Post: Managing Machine Learning Workflows with Scikit-learn Pipelines Part 3: Multiple Models, Pipelines, and Grid Searches

Using Genetic Algorithm for Optimizing Recurrent Neural Networks

[unable to retrieve full-text content]In this tutorial, we will see how to apply a Genetic Algorithm (GA) for finding an optimal window size and a number of units in Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN).
Original Post: Using Genetic Algorithm for Optimizing Recurrent Neural Networks

Managing Machine Learning Workflows with Scikit-learn Pipelines Part 2: Integrating Grid Search

[unable to retrieve full-text content]Another simple yet powerful technique we can pair with pipelines to improve performance is grid search, which attempts to optimize model hyperparameter combinations.
Original Post: Managing Machine Learning Workflows with Scikit-learn Pipelines Part 2: Integrating Grid Search

Is Learning Rate Useful in Artificial Neural Networks?

[unable to retrieve full-text content]This article will help you understand why we need the learning rate and whether it is useful or not for training an artificial neural network. Using a very simple Python code for a single layer perceptron, the learning rate value will get changed to catch its idea.
Original Post: Is Learning Rate Useful in Artificial Neural Networks?