Deep Learning Recurrent Neural Networks In Python Lstm Gru And More Rnn Machine Learning Architectures In Python And Theano Machine Learning In Python < UHD >

h_t = tanh(W_x * x_t + W_h * h_t-1 + b)

Let’s dive in. A standard dense layer assumes no temporal order. It doesn't know that the word following "I ate" is likely food-related, or that yesterday's stock price influences today's. RNNs solve this with a hidden state — a vector that gets passed from one time step to the next. The Simple RNN (Vanilla RNN) The simplest form has a loop. At each time step t , it takes the current input x_t and the previous hidden state h_t-1 , and produces a new hidden state h_t . h_t = tanh(W_x * x_t + W_h * h_t-1 + b) Let’s dive in

import numpy as np from keras.models import Sequential from keras.layers import GRU, Dense def generate_sine_wave(seq_length, num_samples): X, y = [], [] for _ in range(num_samples): start = np.random.uniform(0, 4*np.pi) seq = np.sin(np.linspace(start, start + seq_length, seq_length + 1)) X.append(seq[:-1].reshape(-1, 1)) y.append(seq[-1]) return np.array(X), np.array(y) RNNs solve this with a hidden state —

| Architecture | # Gates | Cell State | Best for | |--------------|---------|------------|-----------| | Simple RNN | 0 | No | Very short sequences | | LSTM | 3 | Yes | Long dependencies, complex data | | GRU | 2 | No | Smaller datasets, faster training | While Theano is no longer actively developed (it was a pioneer, but most have moved to TensorFlow/PyTorch), many legacy systems and research codebases still use it. Here's how you'd build an LSTM for sentiment analysis using Theano with the Keras 1.x API: import numpy as np from keras

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import theano import theano.tensor as T import numpy as np x_t = T.matrix('input') h_prev = T.matrix('hidden_prev') W_xh = theano.shared(np.random.randn(input_dim, hidden_dim)) W_hh = theano.shared(np.random.randn(hidden_dim, hidden_dim)) b_h = theano.shared(np.zeros(hidden_dim))