If you're accustomed to building neural networks with TensorFlow's Keras API, this chapter provides the means to translate those skills to PyTorch's torch.nn module. The focus is on constructing, configuring, and managing neural network architectures in PyTorch, with consistent comparisons to Keras methodologies.

You will learn to:

• Relate Keras Layer objects to their counterparts within torch.nn.Module, PyTorch's fundamental class for all neural network modules.
• Differentiate Keras's Sequential and Functional APIs for model definition from PyTorch's flexible method of subclassing nn.Module.
• Implement and understand common layer types, such as dense (linear), convolutional, and recurrent layers, noting their distinctions and similarities between PyTorch and TensorFlow.
• Identify and apply standard activation functions, available through torch.nn.functional or as specific nn.Module classes.
• Review various weight initialization techniques pertinent to PyTorch models, comparing them to common TensorFlow practices.
• Inspect, access, and alter parameters and sub-modules within a PyTorch model, essential for development and debugging.

Through these comparative explanations and examples, you will develop the practical capability to apply your Keras model-building knowledge effectively in the PyTorch framework, appreciating both the common ground and PyTorch's specific characteristics in network design.