How can RNNs be used for multi-task learning with sequential data?

Aug 27, 2015 ... A recurrent neural network can be thought of as multiple copies of the same network, each passing a message to a successor. Sep 27, 2018 ... I think that is how a NN for trading should be designed. Use current price, OHLC, volume, order depth, and a few indicators as inputs, as well as time of day, ... Introduction. Recurrent neural network is a sequence to sequence model i.e, output of the next is dependent on previous input. RNNs are extensively used for ... May 3, 2023 ... A unique kind of deep learning network called RNN full form Recurrent Neural Network is designed to cope with time series data or data that contains sequences. Mar 3, 2019 ... What happens to the initial hidden state in an RNN layer? · You can just learn the initial hidden state. So it should be randomly initialized ... LSTM excels in sequence prediction tasks, capturing long-term ... principles, and the critical role they play in various applications. What is LSTM? ... network ... a real-time noise suppression algorithm that's small and fast. No expensive GPUs required — it runs easily on a Raspberry Pi ... Jan 1, 2025 ... RNN works as a series of time-unfolding events. Each time the neural network is triggered, it demands an activation function to activate its ... Jun 6, 2017 ... 3 Answers 3 · RNN's "batch size" is to speed up computation (as there're multiple lanes in parallel computation units); it's not mini-batch for ... Unlike feedforward neural networks, which process inputs independently, RNNs utilize recurrent connections, where the output of a neuron at one time step is fed ...