Python Neural Network Journey: Code & Learn-Neural Network Coding Tool

Example

Implementing a neural network with input, hidden, and output layers using Python classes.

Scenario

A user wants to create a neural network to classify handwritten digits from the MNIST dataset. They define the architecture by specifying the number of layers and nodes, creating a foundational structure to which they can apply further training and testing.

Example

Writing functions to perform forward pass and backpropagation calculations for weight updates.

Scenario

During the training phase, the user needs to calculate the outputs for each layer (forward pass) and update the weights based on the error (backpropagation). This function allows them to manually tune and observe how changes affect learning.

Example

Separating different neural network components into modules such as layers, activation functions, and the training loop, with detailed inline comments.

Scenario

When a user wants to experiment with different activation functions or loss calculations, the modular codebase enables easy swapping of components without affecting the entire system. Comprehensive documentation aids in understanding and debugging.