Machine Learning (ML) The ShuffleNet Series (Part 3): Implementation using Pytorch We have covered every step involved in training and testing our ShuffleNet model that performs multi-class image classification. The introduction of the CIFAR 10 dataset and result discussion are also included.
Deep Learning ShuffleNet Series (Part 2): A Comparison with Popular CNN architectures This article covers the comparison of ShuffleNet with some famous architectures. It also covers how well the newer variants of ShuffleNet are as compared to their older counterparts.
Machine Learning (ML) The ShuffleNet Series (Part 1) The breakthrough CNN architecture for object classification in mobile devices, ShuffleNet has been explained in-depth in this article. Its newer and better versions ShuffleNet V2, V2+, Large & X-Large has also been elucidated.
Machine Learning (ML) Deep Learning on 2-Dimensional Images Applying deep learning concepts on images has proved to be one of the important work which has resulted in early detection of diseases resulting in saving millions of life to monitoring activities on the entire Earth We take a look at medical images, Satellite Images and the various Python libraries
Machine Learning (ML) Deep Learning for Medical Imaging and Diagnosis One of the major medical challenges that we face today is the early detection of diseases so that the proper threatment can be applied. This can be solved by applying machine learning to analyse MRI scan, CT Scan, Xray Scans, InfraRed Images, Arthoscopy, UV radiation, Scintigraphy and Ultrasound
Machine Learning (ML) Top deep learning frameworks to explore In this article, we have explored some of the top Deep Learning frameworks that are out there and you should definitely try out. Some of them are TensorFlow, Keras, Caffe, Caffe2, MXNet, CNTK, BigDL, Torch, PyTorch, deeplearn.js and others
Machine Learning (ML) Popular Datasets in Machine Learning Data sets are important in Machine learning as the more better data we have, the better the model. The various popular data sets available for machine learning are ImageNet, MNIST, NIST, CIFAR-10 and YouTube 8M.
Machine Learning (ML) Differences between Torch and PyTorch deep learning libraries We have explored some of the differences between two popular frameworks namely Torch and PyTorch from the view of common origin, current development status, source code and implementation, usage, performance and ONNX support. As development of Torch has been paused, you should go with PyTorch