You can master Computer Vision, Deep Learning, and OpenCV - PyImageSearch
In this tutorial, you will learn how to use Cyclical Learning Rates (CLR) and Keras to train your own neural networks. Using Cyclical Learning Rates you can dramatically reduce the number of experiments required to tune and find an optimal…
Read More of Cyclical Learning Rates with Keras and Deep Learning
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…
Read More of Keras learning rate schedules and decay
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…
Read More of Video classification with Keras and Deep Learning
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. Having a dataset…
Read More of Keras ImageDataGenerator and Data Augmentation
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…
Read More of Change input shape dimensions for fine-tuning with Keras
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…
Read More of Online/Incremental Learning with Keras and Creme
In this tutorial, you will learn how to use Keras and Mask R-CNN to perform instance segmentation (both with and without a GPU). For developing and fine-tuning Mask R-CNN models, having access to rich, diverse datasets is critical. These datasets…
Read More of Keras Mask R-CNN
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.…
Read More of Fine-tuning with Keras and Deep Learning
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…
Read More of Keras: Feature extraction on large datasets with Deep Learning
