In this post, we'll learn 3 Image Classification Projects for Beginners.
A couple of months ago I trained my first image-classification deep neural network. Up until then, I had worked with and around data scientists and AI researchers for several years without ever taking the plunge myself and learning how it all worked. Having recently taken on some machine vision projects, I finally had the push I needed to crack open some Tensorflow and PyTorch tutorials. I am proud to say I’ve mastered the basics of training image classification models and would love to share some interesting and accessible projects to help get others started!
Image classification is the process of a computer accurately predicting the class an image falls under. It will calculate the probability of an image being part of a particular class, such as ‘cat’ or ‘dog’, returning the most likely class as a prediction.
Today, image classification is used in facial-recognition software, medical imaging, and machine vision applications around the world.
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Top Deep Learning Frameworks in 2020: PyTorch vs TensorFlow. This article outlines five factors to help you compare these two major deep learning frameworks; PyTorch and TensorFlow.
Experimental evaluation of how the size of the training dataset affects the performance of a classifier trained through Transfer Learning.
CIFAR 10 Data set using logistic regression. In my previous posts we have gone through. Let us try to solve image classification of CIFAR-10 data set with Logistic regression.
Pytorch is a Deep Learning Library Devoloped by Facebook. it can be used for various purposes such as Natural Language Processing , Computer Vision, etc