Deep Learning

In this tutorial, you will learn how to implement fast, efficient data pipelines for training neural networks using tf.data and TensorFlow. This tutorial is part two in our three part series on the tf.data module: A gentle introduction to tf.data…

In this tutorial, you will learn the basics of TensorFlow’s tf.data module used to build faster, more efficient deep learning data pipelines. This blog post is part one in our three part series on tf.data: A gentle introduction to tf.data…

In this tutorial, you will learn how to use the Keras Tuner package for easy hyperparameter tuning with Keras and TensorFlow. This tutorial is part four in our four-part series on hyperparameter tuning: Introduction to hyperparameter tuning with scikit-learn and…

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 grid search hyperparameters using the scikit-learn machine learning library and the GridSearchCV class. We’ll apply the grid search to a computer vision project. This blog post is part two in our four-part…

In this tutorial, you will implement a CNN using Python and Keras. We’ll start with a quick review of Keras configurations you should keep in mind when constructing and training your own CNNs. We’ll then implement ShallowNet, which as the…

In our previous tutorial, you learned how to train your first Convolutional Neural Network using the Keras library. However, you might have noticed that each time you wanted to evaluate your network or test it on a set of images,…

Now that we’ve trained our model and serialized it, we need to load it from disk. As a practical application of model serialization, I’ll be demonstrating how to classify individual images from the Animals dataset and then display the classified…

The LeNet architecture is a seminal work in the deep learning community, first introduced by LeCun et al. in their 1998 paper, Gradient-Based Learning Applied to Document Recognition. As the name of the paper suggests, the authors’ motivation behind implementing…