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 […]

# Search results for "Convolutional Neural Network"

## Convolutional Neural Network Model Innovations for Image Classification

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 […]

## A Gentle Introduction to Pooling Layers for Convolutional Neural Networks

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 […]

## A Gentle Introduction to Padding and Stride for Convolutional Neural Networks

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 […]

## Stanford Convolutional Neural Networks for Visual Recognition Course (Review)

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 […]

## DeepLearning.AI Convolutional Neural Networks Course (Review)

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 […]

## How to Develop Convolutional Neural Network Models for Time Series Forecasting

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 […]

## Convolutional Neural Networks for Multi-Step Time Series Forecasting

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 […]

## 1D Convolutional Neural Network Models for Human Activity Recognition

Human activity recognition is the problem of classifying sequences of accelerometer data recorded by specialized harnesses or smart phones into known well-defined movements. Classical approaches to the problem involve hand crafting features from the time series data based on fixed-sized windows and training machine learning models, such as ensembles of decision trees. The difficulty is […]

## Deep Convolutional Neural Network for Sentiment Analysis (Text Classification)

Develop a Deep Learning Model to Automatically Classify Movie Reviews as Positive or Negative in Python with Keras, Step-by-Step. Word embeddings are a technique for representing text where different words with similar meaning have a similar real-valued vector representation. They are a key breakthrough that has led to great performance of neural network models on […]