In this post, we'll be learning about Long Short-Term Memory (LSTM) networks with TensorFlow.js and Node.js. We'll go over what LSTM networks are, how they work, and how to train them. By the end of this post, you should have a good understanding of how to use LSTM networks and how they can be applied to different problems.
LSTM networks are a type of recurrent neural network (RNN). RNNs are neural networks that can process sequential data, such as time series data or text. LSTM networks are a special type of RNN that can learn long-term dependencies. This means that they can remember information for long periods of time, which is useful for tasks like prediction and classification.
LSTM networks are composed of LSTM cells. Each LSTM cell has a cell state and an input gate, an output gate, and a forget gate. The cell state is like a memory that can store information for long periods of time. The input gate controls how much information from the current input is stored in the cell state. The output gate controls how much information from the cell state is outputted. The forget gate controls how much information from the previous cell state is forgotten.
LSTM networks use these gates to control the flow of information in the network. This allows them to learn long-term dependencies.
LSTM networks can be trained using a variety of methods. The most common method is to use backpropagation through time (BPTT). BPTT is a method of training RNNs where the gradients are propagated backwards through time. This allows the network to learn from long sequences of data.
In this post, we learned about Long Short-Term Memory networks. We discussed what they are, how they work, and how to train them. LSTM networks are a powerful tool for learning from sequential data.