Chapter 3: Building Simple RNNs

Having covered the core concepts and mathematical underpinnings of simple Recurrent Neural Networks, including the mechanism of Backpropagation Through Time (BPTT), we now shift focus to implementation. This chapter guides you through the practical steps of building your first RNN model using common tools.

You will learn how to:

Set up your Python environment with the necessary deep learning libraries (TensorFlow or PyTorch).
Utilize framework APIs to define SimpleRNN layers.
Correctly manage the input and output tensor shapes expected by recurrent layers, often represented as (batch_size, time_steps, features).
Assemble these layers into a complete sequential model.
Structure a basic training loop to feed data and update model weights based on the loss function.
Apply these steps in a hands-on example involving simple sequence prediction.

By the end of this chapter, you will have translated the theoretical understanding of RNNs into working code, preparing you for more complex architectures and applications.

Sections

3.1 Setting Up the Development Environment
3.2 RNN Cell Implementation
3.3 Using Framework APIs for Simple RNN Layers
3.4 Handling Input and Output Shapes
3.5 Constructing a Basic RNN Model
3.6 Training Loop for RNNs
3.7 Hands-on Practical: Simple Sequence Prediction