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In this tutorial, you will learn about learning rate schedules and decay using Keras. You’ll learn how to use Keras’ standard learning rate decay along with step-based, linear, and polynomial learning rate schedules. When training a neural network, the learning…
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In this tutorial, you will learn how to perform video classification using Keras, Python, and Deep Learning. Specifically, you will learn: The difference between video classification and standard image classification How to train a Convolutional Neural Network using Keras for…
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In today’s tutorial, you will learn how to use Keras’ ImageDataGenerator class to perform data augmentation. I’ll also dispel common confusions surrounding what data augmentation is, why we use data augmentation, and what it does/does not do. Knowing that I…
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In this blog post, you will learn how to perform remote development on a Raspberry Pi (or Amazon EC2 server). You will discover how to configure your host machine to connect to these systems and remotely perform Python development. Today’s…
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In this tutorial, you will learn how to change the input shape tensor dimensions for fine-tuning using Keras. After going through this guide you’ll understand how to apply transfer learning to images with different image dimensions than what the CNN…
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In this tutorial, you will learn how to perform online/incremental learning with Keras and Creme on datasets too large to fit into memory. A few weeks ago I showed you how to use Keras for feature extraction and online learning…
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In this tutorial, you will learn how to use Keras and Mask R-CNN to perform instance segmentation (both with and without a GPU). Using Mask R-CNN we can perform both: Object detection, giving us the (x, y)-bounding box coordinates of…
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In this tutorial, you will learn how to perform fine-tuning with Keras and Deep Learning. We will take a CNN pre-trained on the ImageNet dataset and fine-tune it to perform image classification and recognize classes it was never trained on.…
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In this tutorial, you will learn how to use Keras for feature extraction on image datasets too big to fit into memory. You’ll utilize ResNet-50 (pre-trained on ImageNet) to extract features from a large image dataset, and then use incremental…
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