Neural networks are built with layers connected to each other. There are many different kind of layers. For image related applications, you can always find convolutional layers. It is a layer with very few parameters but applied over a large sized input. It is powerful because it can preserve the spatial structure of the image. […]
Convolutional neural networks have been found successful in computer vision applications. Various network architectures are proposed, and they are neither magical nor hard to understand. In this tutorial, you will make sense of the operation of convolutional layers and their role in a larger convolutional neural network. After finishing this tutorial, you will learn: How […]
Deep learning neural networks are generally opaque, meaning that although they can make useful and skillful predictions, it is not clear how or why a given prediction was made. Convolutional neural networks, have internal structures that are designed to operate upon two-dimensional image data, and as such preserve the spatial relationships for what was learned […]
A Gentle Introduction to the Innovations in LeNet, AlexNet, VGG, Inception, and ResNet Convolutional Neural Networks. Convolutional neural networks are comprised of two very simple elements, namely convolutional layers and pooling layers. Although simple, there are near-infinite ways to arrange these layers for a given computer vision problem. Fortunately, there are both common patterns for […]
Convolutional layers in a convolutional neural network summarize the presence of features in an input image. A problem with the output feature maps is that they are sensitive to the location of the features in the input. One approach to address this sensitivity is to down sample the feature maps. This has the effect of […]
The convolutional layer in convolutional neural networks systematically applies filters to an input and creates output feature maps. Although the convolutional layer is very simple, it is capable of achieving sophisticated and impressive results. Nevertheless, it can be challenging to develop an intuition for how the shape of the filters impacts the shape of the […]
The Stanford course on deep learning for computer vision is perhaps the most widely known course on the topic. This is not surprising given that the course has been running for four years, is presented by top academics and researchers in the field, and the course lectures and notes are made freely available. This is […]
Andrew Ng is famous for his Stanford machine learning course provided on Coursera. In 2017, he released a five-part course on deep learning also on Coursera titled “Deep Learning Specialization” that included one module on deep learning for computer vision titled “Convolutional Neural Networks.” This course provides an excellent introduction to deep learning methods for […]
Convolutional Neural Network models, or CNNs for short, can be applied to time series forecasting. There are many types of CNN models that can be used for each specific type of time series forecasting problem. In this tutorial, you will discover how to develop a suite of CNN models for a range of standard time […]
Given the rise of smart electricity meters and the wide adoption of electricity generation technology like solar panels, there is a wealth of electricity usage data available. This data represents a multivariate time series of power-related variables that in turn could be used to model and even forecast future electricity consumption. Unlike other machine learning […]
