In this tutorial, we'll share How Tensorflow and Keras Used for Image Classification with 6 steps:
Computer Vision is a wide, deep learning field with enormous applications. Image Generation is one of the most curious applications in Computer Vision. Again, Image Generation has a great collection of tasks; to mention, a few can outperform humans. Most image generation tasks are common for videos, too, since a video is a sequence of images.
A few popular Image Generation tasks are:
One deep learning generative model can perform one or more tasks with a few configuration changes. Some famous image generative models are the original versions and the numerous variants of Variational Autoencoder (VAE), and Generative Adversarial Networks (GAN).
This article discusses the concepts behind image generation and the code implementation of Variational Autoencoder with a practical example using TensorFlow Keras. TensorFlow is one of the top preferred frameworks for deep learning processes. Keras is a high-level API built on top of TensorFlow, which is meant exclusively for deep learning.
The following articles may fulfil the prerequisites by giving an understanding of deep learning and computer vision.
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Computer Vision attempts to perform the tasks that a human brain does with the aid of human eyes. Computer Vision is a branch of Deep Learning that deals with images and videos. Computer Vision tasks can be roughly classified into two categories:
Discriminative tasks, in general, are about predicting the probability of occurrence (e.g. class of an image) given probability distribution (e.g. features of an image). Generative tasks, in general, are about generating the probability distribution (e.g. generating an image) given the probability of occurrence (e.g. class of an image) and/or other conditions.
Discriminative Computer Vision finds applications in image classification, object detection, object recognition, shape detection, pose estimation, image segmentation, etc. Generative Computer Vision finds applications in photo enhancement, image synthesis, augmentation, deepfake videos, etc.
This article aims to give a strong foundation to Computer Vision by exploring image classification tasks using Convolutional Neural Networks built with TensorFlow Keras. More importance has been given to both the coding part and the key concepts of theory and math behind each operation. Let’s start our Computer Vision journey!
Readers are expected to have a basic understanding of deep learning. This article, “Getting Started With Deep Learning Using TensorFlow Keras”, helps one grasp the fundamentals of deep learning.
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Keras and Tensorflow are two very popular deep learning frameworks. Deep Learning practitioners most widely use Keras and Tensorflow. Both of these frameworks have large community support. Both of these frameworks capture a major fraction of deep learning production.
Which framework is better for us then?
This blog will be focusing on Keras Vs Tensorflow. There are some differences between Keras and Tensorflow, which will help you choose between the two. We will provide you better insights on both these frameworks.
Keras is a high-level API built on the top of a backend engine. The backend engine may be either TensorFlow, theano, or CNTK. It provides the ease to build neural networks without worrying about the backend implementation of tensors and optimization methods.
Fast prototyping allows for more experiments. Using Keras developers can convert their algorithms into results in less time. It provides an abstraction overs lower level computations.
Tensorflow is a tool designed by Google for the deep learning developer community. The aim of TensorFlow was to make deep learning applications accessible to the people. It is an open-source library available on Github. It is one of the most famous libraries to experiment with deep learning. The popularity of TensorFlow is because of the ease of building and deployment of neural net models.
Major area of focus here is numerical computation. It was built keeping the processing computation power in mind. Therefore we can run TensorFlow applications on almost kind of computer.
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Callbacks are an important type of object in Keras and TensorFlow. They are designed to be able to monitor the model performance in metrics at certain points in the training run and perform some actions that might depend on those performances in metric values.
Keras has provided a number of built-in callbacks, for example,
LearningRateScheduler etc. Apart from these popular built-in callbacks, there is a base class called
Callback which allows us to create our own callbacks and perform some custom actions. In this article, you will learn what is the
Callback base class, what it can do, and how to build your own callbacks.
If you want to learn more about those built-in callbacks, please check out the following article:
#deep-learning #neural-networks #keras #tensorflow #image-classification
Crop and resize image size before upload in laravel using jquery copper js. In this post, i will show you how to crop and resize image size in laravel using jQuery copper js in laravel.
This laravel crop image before upload using cropper js looks like:
Laravel crop image before upload tutorial, follow the following steps and learn how to use cropper js to crop image before uploading in laravel app:
Live Demo Laravel Crop image Before Upload.
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