Post Article 34: Sequence Modeling – RNN, LSTM, and GRU 🧠📚

Article 34: Sequence Modeling – RNN, LSTM, and GRU 🧠📚 Standard Neural Networks assume that all inputs and outputs are independent of each other. But in language, the word "bank" means something different depending on the previous words ("river bank" vs. "money bank"). Sequence models have a memory to remember past information. 1. Recurrent Neural Networks (RNNs) 🔄 An RNN processes a sequence one step at a time. It keeps an internal state (a hidden memory) that gets passed from one step to the next. The Math (Step-by-Step) 🧮 At time step t, The network takes the current input and the previous memory It combines them to make the new memory It uses new memory to produce an output More - Click here... 2. The Big Problem with Standard RNNs ⚠️ Standard RNNs work well for short sequences, but they fail on long sequences due to the Vanishing Gradient Problem. 📉 Vanishing Gradient When backpropagating through many time steps (Backpropagation Through Time), the gradients get multiplied repeatedly by small numbers. 😵 The Result The gradient becomes nearly zero. The model forgets what happened at the beginning of the text or series. 3. Long Short-Term Memory (LSTM) 🚀🧠 To fix the vanishing gradient problem, the LSTM architecture was invented. An LSTM adds a Cell State that acts like a highway for information to flow unchanged, controlled by three mathematical Gates, Forget Gate - Decides what information from the past to throw away. Input Gate - Decides what new information to store in the cell state. Output Gate - Decides what the next hidden state should be based on the updated cell state. More - Click here... 4. Gated Recurrent Unit (GRU) ⚡️ A GRU is a streamlined version of the LSTM. It merges the cell state and hidden state and reduces the three gates down to Two Gates, Reset Gate - Decides how much past information to forget. Update Gate - Combines the jobs of the input gate and forget gate. GRUs have fewer parameters than LSTMs. It making them faster to train while offering nearly identical performance. Summary 📝 RNNs process sequence data step-by-step using a hidden memory. Standard RNNs suffer from Vanishing Gradients, so we use LSTMs (3 gates) or GRUs (2 gates) to retain long-term dependencies effectively. In the next article (Article 35), we start Phase 10: Advanced AI & Transformers, diving into Attention Mechanisms and Transformers! ⚡️🔥 🙊😁 ✍️ @TheInfinityAI