Recurrent neural networks (RNN), a powerful Deep Learning technique for sequential data, is addressed in this article
Recurrent Neural Networks (RNNs) are a subclass of Artificial Neural Networks that, in deep learning, can process a sequence of inputs and maintain its state while processing the subsequent sequence of inputs. Traditional neural networks will analyze one input before moving on to the next, regardless of the order of the inputs. It is necessary to follow the sequential sequence of data, such as time series, in order to comprehend it. Because each input is presumed to be independent of the others in traditional feed-forward networks, each input in a time series setting is reliant on the inputs that came before it.Recurrent neural networks may be applied in a variety of contexts, including:
- Finding the following word or letter
- predicting the price of financial assets in time
- Sports action modeling (predicting the next move in a game of soccer, football, tennis, etc.)
- composition of music
- picture creation
Benefits of an RNN
- Non-linear temporal/sequential connections can be modeled.
- In contrast to an autoregressive process, no lags need to be specified in order to forecast the following value.
The drawbacks of an RNN
- The Vanishing Gradient Issue
- Unsuitable for making long-term predictions
A value from data having a temporal dimension is regressed on earlier values up to a user-specified point to create an autoregressive model. An RNN functions in the same way, but the apparent distinction between the two is that the RNN considers all data, i.e., it does not need the user to choose a certain time period. Model order checking or linearity checks are not necessary with an RNN. To attempt to anticipate the next sequence, it can automatically check the entire dataset. In order to anticipate the output, a neural network has three hidden layers with identical weights, biases, and activation functions. A single recurrent hidden layer may then be created by combining these hidden layers. Now, a recurrent neural retains every step-input from the past and combines it with the step-input from the present.
The gradient of the loss function approaches 0 when more layers with activation functions are added. The gradient descent technique determines the global minimum of the cost function of the network. A gradient that is too tiny for model training shouldn’t affect shallow networks, but as the network becomes larger with more hidden layers, it may do so. The backpropagation algorithm, which finds the network’s derivatives, is used to identify gradients of neural networks. By multiplying along the network, the derivatives of each layer are discovered using the chain rule. The issue is in this area. The gradient may get smaller when using an activation function like the sigmoid function as the number of hidden layers rises. After the model is built, this problem may lead to disastrous outcomes. Utilizing Long-Short Term Memory models with a ReLU activation function has shown to be a straightforward solution to this problem. Recurrent neural networks that can handle long-term dependencies without being impacted by an unstable gradient are known as long-short term memory networks.
