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 […]
Data preparation is required when working with neural networks and deep learning models. Increasingly, data augmentation is also required on more complex object recognition tasks. In this post, you will discover how to use data preparation and data augmentation with your image datasets when developing and evaluating deep learning models in Python with Keras. After […]
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 […]
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 […]
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 […]
Training a neural network or large deep learning model is a difficult optimization task. The classical algorithm to train neural networks is called stochastic gradient descent. It has been well established that you can achieve increased performance and faster training on some problems by using a learning rate that changes during training. In this post, […]
Keras is a Python library for deep learning that wraps the efficient numerical libraries TensorFlow and Theano. Keras allows you to quickly and simply design and train neural networks and deep learning models. In this post, you will discover how to effectively use the Keras library in your machine learning project by working through a […]
Dropout is a simple and powerful regularization technique for neural networks and deep learning models. In this post, you will discover the Dropout regularization technique and how to apply it to your models in Python with Keras. After reading this post, you will know: How the Dropout regularization technique works How to use Dropout on […]
Activation functions play an integral role in neural networks by introducing nonlinearity. This nonlinearity allows neural networks to develop complex representations and functions based on the inputs that would not be possible with a simple linear regression model. Many different nonlinear activation functions have been proposed throughout the history of neural networks. In this post, […]
Deep learning models can take hours, days, or even weeks to train. If the run is stopped unexpectedly, you can lose a lot of work. In this post, you will discover how to checkpoint your deep learning models during training in Python using the Keras library. Let’s get started. Jun/2016: First published Update Mar/2017: Updated […]
