Search results for "summarization"

Histograms of each variable in the training data set

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 […]

Continue Reading 219
Boxplot of top 10 Spot-Checking Algorithms on a Classification Problem

How to Develop a Framework to Spot-Check Machine Learning Algorithms in Python

Spot-checking algorithms is a technique in applied machine learning designed to quickly and objectively provide a first set of results on a new predictive modeling problem. Unlike grid searching and other types of algorithm tuning that seek the optimal algorithm or optimal configuration for an algorithm, spot-checking is intended to evaluate a diverse set of […]

Continue Reading 28
Line plots for the time series in a single trace with trend lines

Indoor Movement Time Series Classification with Machine Learning Algorithms

Indoor movement prediction involves using wireless sensor strength data to predict the location and motion of subjects within a building. It is a challenging problem as there is no direct analytical model to translate the variable length traces of signal strength data from multiple sensors into user behavior. The ‘indoor user movement‘ dataset is a […]

Continue Reading 38
Plot of the Multichannel Convolutional Neural Network For Text

How to Develop a Multichannel CNN Model for Text Classification

A standard deep learning model for text classification and sentiment analysis uses a word embedding layer and one-dimensional convolutional neural network. The model can be expanded by using multiple parallel convolutional neural networks that read the source document using different kernel sizes. This, in effect, creates a multichannel convolutional neural network for text that reads […]

Continue Reading 214
How to Implement Beam Search Decoder for Natural Language Processing

How to Implement a Beam Search Decoder for Natural Language Processing

Natural language processing tasks, such as caption generation and machine translation, involve generating sequences of words. Models developed for these problems often operate by generating probability distributions across the vocabulary of output words and it is up to decoding algorithms to sample the probability distributions to generate the most likely sequences of words. In this […]

Continue Reading 50
Caption Generation with the Inject and Merge Architectures for the Encoder-Decoder Model

Caption Generation with the Inject and Merge Encoder-Decoder Models

Caption generation is a challenging artificial intelligence problem that draws on both computer vision and natural language processing. The encoder-decoder recurrent neural network architecture has been shown to be effective at this problem. The implementation of this architecture can be distilled into inject and merge based models, and both make different assumptions about the role […]

Continue Reading 6