In this post, we’ll cut through the hype and get practical. You'll learn the core RNN architectures (Simple RNN, LSTM, GRU), and implement them in Python using (via the Keras wrapper, which historically used Theano as a backend). Even if you now use TensorFlow or PyTorch, understanding the Theano-era patterns will solidify your fundamentals.
They can remember information for hundreds of steps, making them ideal for text generation, speech recognition, and complex time series. GRU (Gated Recurrent Unit) GRUs are a simpler, faster alternative to LSTMs. They merge the forget and input gates into a single "update gate" and combine the cell state with the hidden state. GRUs perform similarly to LSTMs on many tasks but with fewer parameters. In this post, we’ll cut through the hype and get practical
from keras.models import Sequential from keras.layers import LSTM, GRU, SimpleRNN, Dense, Embedding from keras.preprocessing import sequence max_features = 20000 maxlen = 100 # truncate reviews to 100 words batch_size = 32 Build model model = Sequential() model.add(Embedding(max_features, 128, input_length=maxlen)) model.add(LSTM(128, dropout=0.2, recurrent_dropout=0.2)) # or GRU(128) model.add(Dense(1, activation='sigmoid')) Compile (Theano backend) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) Train model.fit(x_train, y_train, batch_size=batch_size, epochs=5, validation_data=(x_val, y_val)) They can remember information for hundreds of steps,