In this tutorial, you will learn the basics of TensorFlow’s tf.data module used to build faster, more efficient deep learning data pipelines. A good dataset is necessary when working with tf.data in TensorFlow. It allows us to understand how to…

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…

In our previous tutorial, we discussed LeNet, a seminal Convolutional Neural Network in the deep learning and computer vision literature. VGGNet, (sometimes referred to as simply VGG), was first introduced by Simonyan and Zisserman in their 2014 paper, Very Deep…

One concept we have not discussed yet is architecture visualization, the process of constructing a graph of nodes and associated connections in a network and saving the graph to disk as an image (i.e., PNG, JPG, etc.). Nodes in the…

Our entire review of machine learning and neural networks thus far has been leading up to this point: understanding Convolutional Neural Networks (CNNs) and the role they play in deep learning. In traditional feedforward neural networks, each neuron in the…

CNN Building Blocks Neural networks accept an input image/feature vector (one input node for each entry) and transform it through a series of hidden layers, commonly using nonlinear activation functions. Each hidden layer is also made up of a set…