In this tutorial, we will use transfer learning and fine-tuning with Python, Keras, and deep learning to classify images from the dogs vs cats dataset. We will use the VGG 19 model pre-trained on ImageNet as our base model for feature extraction.

In deep learning, a convolutional neural network is a class of deep neural networks that have been used with great success in computer vision tasks such as image classification, object detection, image segmentation, ...

In this tutorial, I will show you how to read, display, and write images using OpenCV's built-in functions.

In today's post, I will show you how to use Python and the internet of things to control a smart device. The internet of things is a system of interrelated sensors, software, and other technologies that can be connected to the internet ...

In this tutorial, we will tackle the Fashion MNIST dataset to train a neural network that will classify images of clothing. The dataset contains 70,000 grayscale images of 28 × 28 pixels each in 10 categories.

In OpenCV, images are simply Numpy arrays. So we can use Numpy array slicing to crop an image and remove the part we are not interested in.

Celery uses a result backend to keep track of the tasks’ states. In this tutorial, we are going to use the RPC (RabbitMQ/AMQP) result backend to store and retrieve the states of tasks.

There are so many online courses on Django that it's hard to find a course that's high quality and up-to-date. In this article, I will give you my selection of 6 of the best online courses to learn Django.