Human activity recognition is the problem of classifying sequences of accelerometer data recorded by specialized harnesses or smart phones into known well-defined movements. It is a challenging problem given the large number of observations produced each second, the temporal nature of the observations, and the lack of a clear way to relate accelerometer data to […]

# Search results for "Time Series RNN"

## Encoder-Decoder Recurrent Neural Network Models for Neural Machine Translation

The encoder-decoder architecture for recurrent neural networks is the standard neural machine translation method that rivals and in some cases outperforms classical statistical machine translation methods. This architecture is very new, having only been pioneered in 2014, although, has been adopted as the core technology inside Google’s translate service. In this post, you will discover […]

## CNN Long Short-Term Memory Networks

Gentle introduction to CNN LSTM recurrent neural networks with example Python code. Input with spatial structure, like images, cannot be modeled easily with the standard Vanilla LSTM. The CNN Long Short-Term Memory Network or CNN LSTM for short is an LSTM architecture specifically designed for sequence prediction problems with spatial inputs, like images or videos. […]

## Mini-Course on Long Short-Term Memory Recurrent Neural Networks with Keras

Long Short-Term Memory (LSTM) recurrent neural networks are one of the most interesting types of deep learning at the moment. They have been used to demonstrate world-class results in complex problem domains such as language translation, automatic image captioning, and text generation. LSTMs are different to multilayer Perceptrons and convolutional neural networks in that they […]

## Long Short-Term Memory Networks With Python

Long Short-Term Memory Networks With Python Develop Deep Learning Models for your Sequence Prediction Problems Sequence Prediction is…important, overlooked, and HARD Sequence prediction is different to other types of supervised learning problems. The sequence imposes an order on the observations that must be preserved when training models and making predictions. There are 4 main types of […]

## Attention in Long Short-Term Memory Recurrent Neural Networks

The Encoder-Decoder architecture is popular because it has demonstrated state-of-the-art results across a range of domains. A limitation of the architecture is that it encodes the input sequence to a fixed length internal representation. This imposes limits on the length of input sequences that can be reasonably learned and results in worse performance for very […]

## How to Prepare Sequence Prediction for Truncated BPTT in Keras

Recurrent neural networks are able to learn the temporal dependence across multiple timesteps in sequence prediction problems. Modern recurrent neural networks like the Long Short-Term Memory, or LSTM, network are trained with a variation of the Backpropagation algorithm called Backpropagation Through Time. This algorithm has been modified further for efficiency on sequence prediction problems with […]

## Learn to Add Numbers with an Encoder-Decoder LSTM Recurrent Neural Network

Long Short-Term Memory (LSTM) networks are a type of Recurrent Neural Network (RNN) that are capable of learning the relationships between elements in an input sequence. A good demonstration of LSTMs is to learn how to combine multiple terms together using a mathematical operation like a sum and outputting the result of the calculation. A […]

## Miguel Peralvo

A nice introduction to Deep Learning with Keras. I found the book quite didactic and entertaining. Theano and Tensorflow are explored briefly in some specific chapters at the beginning of the book, but most of the material covers how to use Keras effectively with CNNs and RNNs. I found the Time Series and model improvement […]

## Crash Course in Recurrent Neural Networks for Deep Learning

Another type of neural network is dominating difficult machine learning problems involving sequences of inputs: recurrent neural networks. Recurrent neural networks have connections that have loops, adding feedback and memory to the networks over time. This memory allows this type of network to learn and generalize across sequences of inputs rather than individual patterns. A […]