Ai Rnn Lstm — Free AI & ML Tutorial
Learn Ai Rnn Lstm in AI & ML with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
TL;DR: Learn Ai Rnn Lstm in AI & ML with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
Written & reviewed by the Syllab.in Academic Team (CBSE/NCERT subject experts) · Updated
Ai Rnn Lstm in AI & ML
Recurrent Neural Networks (RNNs) process sequences by maintaining a hidden state that is updated at each step. Unlike feedforward networks, the output at each step depends on both the current input and memory of previous inputs — making RNNs ideal for time series, text, and audio.
Problem with vanilla RNNs: Vanishing gradients over long sequences. Gradients diminish exponentially as they travel back through time, making it impossible to learn dependencies between distant time steps.
LSTM (Long Short-Term Memory): Introduced by Hochreiter and Schmidhuber in 1997. LSTMs have three gates: Forget gate (decide what to forget from memory), Input gate (decide what new info to store), Output gate (decide what to output from memory). These gates control information flow and solve the vanishing gradient problem.
Applications: Language modelling, machine translation, speech recognition, music generation, stock price prediction, video captioning. Today, Transformers have largely replaced LSTMs for NLP, but LSTMs remain important for real-time sequential processing.
Ai Rnn Lstm — Syntax
# RNN hidden state update:
# h_t = tanh(W_h * h_{t-1} + W_x * x_t + b)
# output_t = W_out * h_t
#
# LSTM gates (simplified):
# f_t = sigmoid(W_f * [h_{t-1}, x_t] + b_f) # forget
# i_t = sigmoid(W_i * [h_{t-1}, x_t] + b_i) # input
# o_t = sigmoid(W_o * [h_{t-1}, x_t] + b_o) # output
# c_t = f_t *
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