Search results for "Recurrent Neural Network"

Understanding Stateful LSTM Recurrent Neural Networks in Python with Keras

Understanding Stateful LSTM Recurrent Neural Networks in Python with Keras

A powerful and popular recurrent neural network is the long short-term model network or LSTM. It is widely used because the architecture overcomes the vanishing and exposing gradient problem that plagues all recurrent neural networks, allowing very large and very deep networks to be created. Like other recurrent neural networks, LSTM networks maintain state, and […]

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Sequence Classification with LSTM Recurrent Neural Networks in Python with Keras

Sequence Classification with LSTM Recurrent Neural Networks in Python with Keras

Sequence classification is a predictive modeling problem where you have some sequence of inputs over space or time and the task is to predict a category for the sequence. What makes this problem difficult is that the sequences can vary in length, be comprised of a very large vocabulary of input symbols and may require […]

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Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras

Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras

Time series prediction problems are a difficult type of predictive modeling problem. Unlike regression predictive modeling, time series also adds the complexity of a sequence dependence among the input variables. A powerful type of neural network designed to handle sequence dependence is called recurrent neural networks. The Long Short-Term Memory network or LSTM network is […]

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Crash Course in Recurrent Neural Networks for Deep Learning

Crash Course in Recurrent Neural Networks for Deep Learning

There is another type of neural network that is dominating difficult machine learning problems that involve sequences of inputs called 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 […]

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Scatter Plot of Binary Classification Dataset with 1 to 100 Class Imbalance

How to Develop a Cost-Sensitive Neural Network for Imbalanced Classification

Deep learning neural networks are a flexible class of machine learning algorithms that perform well on a wide range of problems. Neural networks are trained using the backpropagation of error algorithm that involves calculating errors made by the model on the training dataset and updating the model weights in proportion to those errors. The limitation […]

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Line Plot Classification Accuracy of MLP With Batch Normalization After Activation Function on Train and Test Datasets Over Training Epochs

How to Accelerate Learning of Deep Neural Networks With Batch Normalization

Batch normalization is a technique designed to automatically standardize the inputs to a layer in a deep learning neural network. Once implemented, batch normalization has the effect of dramatically accelerating the training process of a neural network, and in some cases improves the performance of the model via a modest regularization effect. In this tutorial, […]

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How to Calibrate Probabilities for Imbalanced Classification

A Gentle Introduction to Batch Normalization for Deep Neural Networks

Training deep neural networks with tens of layers is challenging as they can be sensitive to the initial random weights and configuration of the learning algorithm. One possible reason for this difficulty is the distribution of the inputs to layers deep in the network may change after each mini-batch when the weights are updated. This […]

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