Neural Network Model Trainer
Simulate and train a neural network model by providing your data and network details. Explore the basics of neural networks and model training.
Input Parameters
Example: [[0,0],[0,1],[1,0],[1,1]]
Specify the size of hidden layers.
Specify parameters like epochs, learning rate, etc.
Simulation Output
Trained Model
Performance Metrics
| Metric | Value |
|---|---|
| Accuracy | |
| Loss | |
| Precision | |
| Recall |
About Neural Network Models
A Neural Network Model is a computational model inspired by the structure and function of biological neural networks. It consists of interconnected nodes or neurons organized in layers. These networks learn from input data to make predictions or decisions. In this tool, you can simulate the training process by providing training data, defining the network's architecture (number of neurons), and setting training parameters. The output provides a simplified representation of a trained model and its performance metrics like accuracy, loss, precision, and recall. This tool is designed for educational purposes to help understand the basic concepts of neural networks without requiring actual complex computations.
- Training Data: The dataset used to train the neural network.
- Network Architecture: The structure of the network, including the number of layers and neurons.
- Training Parameters: Settings that control the training process, such as learning rate and epochs.
- Performance Metrics: Measures used to evaluate the performance of the trained model.