AI News, Crash Course in Recurrent Neural Networks for Deep Learning
- On 6. juni 2018
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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.
powerful type of Recurrent Neural Network called the Long Short-Term Memory Network has been shown to be particularly effective when stacked into a deep configuration, achieving state-of-the-art results on a diverse array of problems from language translation to automatic captioning of images and videos.
In this post you will get a crash course in recurrent neural networks for deep learning, acquiring just enough understanding to start using LSTM networks in Python with Keras.
Support for sequences in neural networks is an important class of problem and one where deep learning has recently shown impressive results State-of-the art results have been using a type of network specifically designed for sequence problems called recurrent neural networks.
This allows the cyclic graph of a recurrent neural network to be turned into an acyclic graph like a classic feed-forward neural network, and Backpropagation can be applied.
When Backpropagation is used in very deep neural networks and in unrolled recurrent neural networks, the gradients that are calculated in order to update the weights can become unstable.
This problem is alleviated in deep multilayer Perceptron networks through the use of the Rectifier transfer function, and even more exotic but now less popular approaches of using unsupervised pre-training of layers.
In recurrent neural network architectures, this problem has been alleviated using a new type of architecture called the Long Short-Term Memory Networks that allows deep recurrent networks to be trained.
As such it can be used to create large (stacked) recurrent networks, that in turn can be used to address difficult sequence problems in machine learning and achieve state-of-the-art results.
A unit operates upon an input sequence and each gate within a unit uses the sigmoid activation function to control whether they are triggered or not, making the change of state and addition of information flowing through the unit conditional.
There are three types of gates within a memory unit: Each unit is like a mini state machine where the gates of the units have weights that are learned during the training procedure.
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