In this tutorial, you will learn how to use the Keras Tuner package for easy hyperparameter tuning with Keras and TensorFlow. A sizable dataset is necessary when working with hyperparameter tuning. It allows us to understand the effects of different…

In this tutorial, you will learn how to tune the hyperparameters of a deep neural network using scikit-learn, Keras, and TensorFlow. This tutorial is part three in our four-part series on hyperparameter tuning: Introduction to hyperparameter tuning with scikit-learn and…

In this tutorial, you will learn how to use the GridSearchCV class to do grid search hyperparameter tuning using the scikit-learn machine learning library. We’ll apply the grid search to a computer vision project. This blog post is part two…

In this tutorial, you will learn how to tune machine learning model hyperparameters with scikit-learn and Python. This tutorial is part one in a four-part series on hyperparameter tuning: Introduction to hyperparameter tuning with scikit-learn and Python (today’s post) Grid…

In this tutorial you will learn how to use dlib’s find_min_global function to optimize the options and hyperparameters to dlib’s shape predictor, yielding a more accurate model. A few weeks ago I published a two-part series on using dlib to…

In this tutorial, you will learn how to optimally tune dlib’s shape predictor hyperparameters and options to obtain a shape predictor that balances speed, accuracy, and model size. Today is part two in our two-part series on training custom shape…

In last week’s post, I introduced the k-NN machine learning algorithm which we then applied to the task of image classification. Using the k-NN algorithm, we obtained 57.58% classification accuracy on the Kaggle Dogs vs. Cats dataset challenge: The question…