Building an LSTM network from scratch involves several key steps, including understanding the architecture, implementing the necessary components, and training the model on your data.

First, it’s essential to grasp the LSTM architecture. LSTMs are a type of Recurrent Neural Network (RNN) designed to handle sequential data effectively. They include memory cells that help retain information over long sequences, which is crucial for tasks like time series forecasting and natural language processing ²⁵.

Next, you will need to implement the LSTM cell, which consists of various gates: the input gate, forget gate, and output gate. These gates control the flow of information into and out of the memory cell, allowing the network to learn long-term dependencies ³⁵.

Once the architecture is in place, you can proceed to train your LSTM model using a suitable dataset. This involves preprocessing the data, defining a loss function, and using an optimizer to adjust the weights during training ¹.

Finally, after training, you can evaluate the model’s performance and make adjustments as necessary to improve accuracy.