Last Updated on October 3, 2019 You can learn a lot about neural networks and deep learning models by observing their performance over time during training. Keras is a powerful library in Python that provides a clean interface for creating deep learning models and wraps the more technical TensorFlow and Theano backends. In this post you will discover how you can review and visualize the performance of deep learning models over time during training in Python with Keras. Kick-start your […]

Last Updated on August 27, 2020 A simple and powerful regularization technique for neural networks and deep learning models is dropout. 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 your input layers. How to use dropout on your hidden layers. How to tune the dropout level on your […]

Last Updated on August 27, 2020 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 you will discover how you can use different learning rate schedules for your neural network models in Python using […]

Last Updated on August 19, 2019 Convolutional Neural Networks are a powerful artificial neural network technique. These networks preserve the spatial structure of the problem and were developed for object recognition tasks such as handwritten digit recognition. They are popular because people are achieving state-of-the-art results on difficult computer vision and natural language processing tasks. In this post you will discover Convolutional Neural Networks for deep learning, also called ConvNets or CNNs. After completing this crash course you will know: […]

Last Updated on August 27, 2020 A popular demonstration of the capability of deep learning techniques is object recognition in image data. The “hello world” of object recognition for machine learning and deep learning is the MNIST dataset for handwritten digit recognition. In this post you will discover how to develop a deep learning model to achieve near state of the art performance on the MNIST handwritten digit recognition task in Python using the Keras deep learning library. After completing […]

Last Updated on September 13, 2019 Data preparation is required when working with neural network 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 reading this post, you will know: About the image augmentation API provide by Keras and how to use it […]