Search results for "Convolutional Neural Network"

Visualization of the Feature Maps Extracted From the First Convolutional Layer in the VGG16 Model

How to Visualize Filters and Feature Maps in Convolutional Neural Networks

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 […]

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Architecture of the Residual Network for Object Photo Classification

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 […]

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A Gentle Introduction to Padding and Stride for Convolutional Neural Networks

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 […]

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Convolutional Neural Networks Taught by Andrew Ng

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 […]

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How to Develop Convolutional Neural Networks for Multi-Step Time Series Forecasting

How to Develop 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 […]

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Histograms of each variable in the training data set

How to Develop 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 […]

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How to Develop a Word Embedding Model for Predicting Movie Review Sentiment

How to Develop a 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 […]

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