Fool-proof RNN explanation | What are RNNs, how do they work?

Step-by-Step Tutorial: Understanding Recurrent Neural Networks (RNNs)

1. Introduction to Neural Networks:

• In a traditional neural network, there are input, hidden, and output layers where information flows sequentially.
• Unlike traditional neural networks, recurrent neural networks (RNNs) are designed to process sequential data by repeating the same operation in each time step.

2. Structure of a Recurrent Neural Network:

• In an RNN, each time step processes one feature of the input data sequentially.
• The output of each time step is passed as input to the next time step, creating a continuous flow of information.
• RNNs use the same set of weights for both input and output, making them suitable for sequential data processing.

3. Calculation in RNNs:

• The output of an RNN is calculated using a formula involving weights, biases, and an activation function like hyperbolic tangent.
• The output of each time step can be passed directly to the next step or modified before passing it forward.

4. Types of RNN Architectures:

• Sequence-to-sequence: Input at each time step corresponds to an output at that step, useful for tasks like price forecasting.
• Sequence-to-single: Only one output is generated at the end of the sequence, suitable for tasks like sentiment analysis.
• Vector-to-sequence: Input is a single vector, and the output is a sequence, used for tasks like image captioning.
• Encoder-decoder: Input is given initially, and output is generated without further inputs, beneficial for translation tasks.

5. Training RNNs:

• RNNs are trained using backpropagation through time, where gradients are calculated and weights are updated based on the error.
• Handling unstable gradients in RNNs can be managed by using techniques like layer normalization instead of batch normalization.

6. Challenges and Solutions in RNNs:

• RNNs may face issues like vanishing gradients and forgetting long sequences.
• To address these challenges, more advanced RNN architectures like LSTM and GRU cells are used to retain long-term dependencies in the data.

7. LSTM and GRU Cells:

• LSTM cells have separate mechanisms for long-term and short-term memory storage, controlled by gates like forget, input, and output gates.
• GRU cells are simpler versions of LSTM cells with fewer components but still capable of capturing long-term dependencies in data.

8. Further Learning:

• To deepen your understanding of RNNs and deep learning, consider exploring courses like "Deep Learning 101."
• Stay curious and continue learning about the fascinating world of neural networks and their applications.

By following these steps, you can grasp the fundamentals of recurrent neural networks and their significance in processing sequential data effectively.