Machine Learning (ML) Saving and Reusing Machine Learning Models In this article, we will be learning about various ways of saving and reusing machine learning models.
Machine Learning (ML) Types of Generative Adversarial Networks (GANs) In this article, we cover the types of GAN's. A Generative Adversarial Network is a machine learning algorithm that is capable of generating new training datasets.
Machine Learning (ML) Posenet Model in ML In this article, we will learn about pre-trained model PoseNet in detail which will be consisting of need and working of posenet, operations possible on it, its application, and possible improvement over existing posenet model.
Machine Learning (ML) Transposed Convolution Transposed convolution is also known as upsampled convolution, which refers to the task it accomplishes, which is to upsample the input feature map.
Machine Learning (ML) Grouped and Shuffled Grouped Convolution In this article, we have explored the variant of Convolution named Grouped and Shuffled Grouped Convolution.
Machine Learning (ML) SpineNet SpineNet proposes an alternative to ResNet50, a variant of the ResNet model which uses 50 layers of deep convolutional network (hence "50" in its name). It intends to disrupt the CNN architecture from a high level which has not changed over the years.
Machine Learning (ML) RefineDet model RefineDet model is a popular Deep Learning model that is used for Object Detection applications as an alternative to SSD and YOLO based CNN models.
Machine Learning (ML) Flattened Convolutional Neural Network In this article, we have explored the idea of Flattened Convolutional Neural Network and the problem of conventional CNN it solves.
Machine Learning (ML) RefineNet Model In this article, we have explained RefineNet Model in depth which is a deep learning model used for Semantic Segmentation.
Machine Learning (ML) Person re-identification ReID In this article, we have explored the idea behind Person re-identification ReID applications, techniques for ReID and real world applications.
Machine Learning (ML) Commonly Used Neural Networks We have explored the commonly used Neural Networks like Hebbian Neural Networks, Auto-Associative Neural Networks, Hopfield Neural Networks, Radial Basis Function Neural Networks and much more.
Machine Learning (ML) Conditional Generative Adversarial Nets In this article, we have explained the concept of Conditional Generative Adversarial Nets in depth.
Deep Learning What is Neural Network and Deep Learning? Deep Learning has become quite a buzzword in recent years. It has taken over in all applications from tasks like image recognition, chatbots like Alexa and Google Assistant to defeating world champions in a complex games like Go and Dota 2.
Machine Learning (ML) Local response normalization (LRN) In this article, we have explained the concept of Local response normalization (LRN) in depth along with comparison with Batch Normalization.
Deep Learning Neural Architecture Search (NAS) The aim of this article is to provide a clear and intuitive understanding of the deep learning paradigm known as Neural Architecture Search (NAS).
Machine Learning (ML) Supervised, Unsupervised and Semi-Supervised Learning In this article, we will learn more about the differences between Supervised, Unsupervised and Semi-Supervised Learning.
Deep Learning Neural Collaborative Filtering (NCF) - Part 1 This research marks the beginning of neural networks for collaborative filtering using implicit data. It proves the inability of linear models and simple inner product to understand the complex user-item interactions. We introduce the NCF architecture in its 3 instantiations - GMF, MLP and NeuMF.
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