Scenarios where a simple Neural Network work like this might be useful:
Regression with Multiple Features If you have multiple input features and you want to predict a continuous output, this network can learn the appropriate weights for each feature. For instance:
- Predicting fuel efficiency of a car based on features like engine size, horsepower, and weight.
- Predicting sales based on multiple factors like marketing spend, seasonality, and economic indicators.
Binary Classification With slight modification (e.g., adding a Sigmoid activation to the output layer), you could use this network for binary classification tasks. For example:
- Classifying whether an email is spam or not based on features like word frequency and sender information.
Multi-Feature Time Series Forecasting If you have time series data with multiple variables, you can feed it into this simple network to predict future values based on past trends. For instance:
- Predicting stock prices based on multiple features like historical prices, trading volume, and economic data.
Training and Optimization (Next Steps) The provided code only defines the network and performs a forward pass, but to use this model for real-world tasks, you would need to:
- Define a loss function (e.g., Mean Squared Error for regression or Cross-Entropy Loss for classification).
- Train the network using an optimizer like Stochastic Gradient Descent (SGD), Adam, or another optimization algorithm.
- Backpropagate the gradients to update the model’s weights using gradient descent.