Search results for "Time Series RNN"

On the Suitability of Long Short-Term Memory Networks for Time Series Forecasting

On the Suitability of Long Short-Term Memory Networks for Time Series Forecasting

Long Short-Term Memory (LSTM) is a type of recurrent neural network that can learn the order dependence between items in a sequence. LSTMs have the promise of being able to learn the context required to make predictions in time series forecasting problems, rather than having this context pre-specified and fixed. Given the promise, there is […]

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Time Series Forecasting with the Long Short-Term Memory Network in Python

Time Series Forecasting with the Long Short-Term Memory Network in Python

The Long Short-Term Memory recurrent neural network has the promise of learning long sequences of observations. It seems a perfect match for time series forecasting, and in fact, it may be. In this tutorial, you will discover how to develop an LSTM forecast model for a one-step univariate time series forecasting problem. After completing this […]

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Adding A Custom Attention Layer To Recurrent Neural Network In Keras

Deep learning networks have gained immense popularity in the past few years. The ‘attention mechanism’ is integrated with the deep learning networks to improve their performance. Adding attention component to the network has shown significant improvement in tasks such as machine translation, image recognition, text summarization and similar applications. This tutorial shows how to add […]

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An Introduction To Recurrent Neural Networks And The Math That Powers Them

When it comes to sequential or time series data, traditional feedforward networks cannot be used for learning and prediction. A mechanism is required that can retain past or historic information to forecast the future values. Recurrent neural networks or RNNs for short are a variant of the conventional feedforward artificial neural networks that can deal […]

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Learning Curves of Cross-Entropy Loss for a Deep Learning Model

TensorFlow 2 Tutorial: Get Started in Deep Learning with tf.keras

Predictive modeling with deep learning is a skill that modern developers need to know. TensorFlow is the premier open-source deep learning framework developed and maintained by Google. Although using TensorFlow directly can be challenging, the modern tf.keras API brings Keras’s simplicity and ease of use to the TensorFlow project. Using tf.keras allows you to design, […]

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Overview of Course Structure

Practical Deep Learning for Coders (Review)

Practical deep learning is a challenging subject in which to get started. It is often taught in a bottom-up manner, requiring that you first get familiar with linear algebra, calculus, and mathematical optimization before eventually learning the neural network techniques. This can take years, and most of the background theory will not help you to […]

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Line Plots of Accuracy on Train and Test Datasets While Training With Dropout Regularization

How to Reduce Overfitting With Dropout Regularization in Keras

Dropout regularization is a computationally cheap way to regularize a deep neural network. Dropout works by probabilistically removing, or “dropping out,” inputs to a layer, which may be input variables in the data sample or activations from a previous layer. It has the effect of simulating a large number of networks with very different network […]

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Depiction of CNN Model for Accelerompter Data

Deep Learning Models for Human Activity Recognition

Human activity recognition, or HAR, is a challenging time series classification task. It involves predicting the movement of a person based on sensor data and traditionally involves deep domain expertise and methods from signal processing to correctly engineer features from the raw data in order to fit a machine learning model. Recently, deep learning methods […]

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