Tensorflow implementation of the Vision Transformer (ViT) presented in An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, where the authors show that Transformers applied directly to image patches and pre-trained on large datasets work really well on image classification.
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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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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We will go over what is the difference between pytorch, tensorflow and keras in this video. Pytorch and Tensorflow are two most popular deep learning frameworks. Pytorch is by facebook and Tensorflow is by Google. Keras is not a full fledge deep learning framework, it is just a wrapper around Tensorflow that provides some convenient APIs.
#pytorch #tensorflow #keras #python #deep-learning
Welcome to DataFlair Keras Tutorial. This tutorial will introduce you to everything you need to know to get started with Keras. You will discover the characteristics, features, and various other properties of Keras. This article also explains the different neural network layers and the pre-trained models available in Keras. You will get the idea of how Keras makes it easier to try and experiment with new architectures in neural networks. And how Keras empowers new ideas and its implementation in a faster, efficient way.
Keras is an open-source deep learning framework developed in python. Developers favor Keras because it is user-friendly, modular, and extensible. Keras allows developers for fast experimentation with neural networks.
Keras is a high-level API and uses Tensorflow, Theano, or CNTK as its backend. It provides a very clean and easy way to create deep learning models.
Keras has the following characteristics:
The following major benefits of using Keras over other deep learning frameworks are:
Before installing TensorFlow, you should have one of its backends. We prefer you to install Tensorflow. Install Tensorflow and Keras using pip python package installer.
The basic data structure of Keras is model, it defines how to organize layers. A simple type of model is the Sequential model, a sequential way of adding layers. For more flexible architecture, Keras provides a Functional API. Functional API allows you to take multiple inputs and produce outputs.
It allows you to define more complex models.
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Playlist of 12 Videos - Deep Learning / Computer Vision Algorithm Implementations
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