Simplified: All About Neural Networks

explore the structure of neural networks, including neurons as mathematical functions, synapses as connections, and the role of hidden layers in processing inputs to outputs.

Feeding forward in neural networks is illustrated by showing how inputs and weights compute neuron outputs across layers to propagate the signal through the network.

Explore backwards propagation in neural networks by applying gradient descent with a squared error cost function to adjust weights and propagate error layer by layer.

Analyze the loan data set to prepare a case study and outline steps for implementing a neural network. Review features like loan purpose, interest, income log, dti, fico.

Install keras in Spyder by setting up the environment, updating the index, and selecting Python 3.6; add keras base and preprocessing, then reinstall Spyder to begin building your neural network.

Import core packages and load a csv dataset into the neural network project. Standardize data by subtracting the column mean and dividing by the standard deviation along axis 0.

Process the dataset for a neural network by one-hot encoding the purpose column into seven categories, standardizing features to a 0–1 scale, and concatenating into the final dataset.

Instantiate the model, build a three-layer network with relu activation, perform train/test split, compile with an optimizer and mean squared error metrics, then train for 20 epochs with batch 500.

Run the neural network for 20 epochs, observe mean absolute error and validation loss decrease, evaluate predictions against test data, and plot training loss to confirm optimization.