When you build and train a Keras deep learning model, you can provide the training data in several different ways. Presenting the data as a NumPy array or a TensorFlow tensor is common. Another way is to make a Python generator function and let the training loop read data from it. Yet another way of providing data is to use tf.data dataset. In this tutorial, you will see how you can use the tf.data dataset for a Keras model. After finishing […]

Convolutional neural networks have been found successful in computer vision applications. Various network architectures are proposed, and they are neither magical nor hard to understand. In this tutorial, you will make sense of the operation of convolutional layers and their role in a larger convolutional neural network. After finishing this tutorial, you will learn: How convolutional layers extract features from an image How different convolutional layers can stack up to build a neural network Let’s get started. Understanding the design […]

The loss metric is very important for neural networks. As all machine learning models are one optimization problem or another, the loss is the objective function to minimize. In neural networks, the optimization is done with gradient descent and backpropagation. But what are loss functions, and how are they affecting your neural networks? In this post, you will learn what loss functions are and delve into some commonly used loss functions and how you can apply them to your neural […]

When you work on a machine learning problem related to images, not only do you need to collect some images as training data, but you also need to employ augmentation to create variations in the image. It is especially true for more complex object recognition problems. There are many ways for image augmentation. You may use some external libraries or write your own functions for that. There are some modules in TensorFlow and Keras for augmentation too. In this post, […]