Kas te olete töötanud rekurrentsete närvivõrkudega?

Recurrent neural networks (RNN) are used for processing sequential data such as text, time series, or audio. In my practice, I have used RNNs for tasks like time series prediction and text analysis, for example, for text generation or sequence classification.

RNNs are distinguished by their cyclic connections, which allow considering the context of previous elements in the sequence. However, classical RNNs can suffer from the vanishing gradient problem, so their improved versions like LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) are often used.

Example of a simple RNN in Python using Keras:

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import SimpleRNN, Dense

model = Sequential([
    SimpleRNN(50, input_shape=(timesteps, features)),
    Dense(1, activation='sigmoid')
])
model.compile(optimizer='adam', loss='binary_crossentropy')

Here, timesteps is the length of the sequence, and features is the number of features per step.