What is BPTT algorithm?

Backpropagation Through Time, or BPTT, is the application of the Backpropagation training algorithm to recurrent neural network applied to sequence data like a time series. A recurrent neural network is shown one input each timestep and predicts one output. Conceptually, BPTT works by unrolling all input timesteps.

What is the difference between backpropagation algorithm and Backpropagation Through Time BPTT algorithm?

The Backpropagation algorithm is suitable for the feed forward neural network on fixed sized input-output pairs. The Backpropagation Through Time is the application of Backpropagation training algorithm which is applied to the sequence data like the time series.

Does real time recurrent learning is faster than BPTT?

BPTT tends to be significantly faster for training recurrent neural networks than general-purpose optimization techniques such as evolutionary optimization.

How does bidirectional RNN work?

Bidirectional recurrent neural networks (BRNN) connect two hidden layers of opposite directions to the same output. With this form of generative deep learning, the output layer can get information from past (backwards) and future (forward) states simultaneously.

What is RNN architecture?

A recurrent neural network (RNN) is a special kind of artificial neural network that permits continuing information related to past knowledge by utilizing a special kind of looped architecture. They are employed in many areas regarding data with sequences, such as predicting the next word of a sentence.

What is the difference between BPTT and RTRL algorithms?

A more computationally expensive online variant is called “Real-Time Recurrent Learning” or RTRL, which is an instance of automatic differentiation in the forward accumulation mode with stacked tangent vectors. Unlike BPTT, this algorithm is local in time but not local in space.

What is vanishing and exploding gradient?

Following are some signs that can indicate that our gradients are exploding/vanishing : Exploding. Vanishing. There is an exponential growth in the model parameters. The parameters of the higher layers change significantly whereas the parameters of lower layers would not change much (or not at all).

Does RNN use back propagation?

You see, a RNN essentially processes sequences one step at a time, so during backpropagation the gradients flow backward across time steps. This is called backpropagation through time. So, the gradient wrt the hidden state and the gradient from the previous time step meet at the copy node where they are summed up.

What is the main difference between RNN and bidirectional RNN?

RNN has the limitation that it processes inputs in strict temporal order. This means current input has context of previous inputs but not the future. Bidirectional RNN ( BRNN ) duplicates the RNN processing chain so that inputs are processed in both forward and reverse time order.

Is bidirectional LSTM better than LSTM?

It can also be helpful in Time Series Forecasting problems, like predicting the electric consumption of a household. However, we can also use LSTM in this but Bidirectional LSTM will also do a better job in it.

What is difference between CNN and RNN?

A CNN has a different architecture from an RNN. CNNs are “feed-forward neural networks” that use filters and pooling layers, whereas RNNs feed results back into the network (more on this point below). In CNNs, the size of the input and the resulting output are fixed.