From e6fc1258d81840ba345a6796b4a42bead8092c82 Mon Sep 17 00:00:00 2001
From: Nikolas <147613528+stamatisn@users.noreply.github.com>
Date: Fri, 3 Jan 2025 13:49:20 +0200
Subject: [PATCH] Create README

 This file is the README I told I will upload to explain the what the ML code does in (#102).
 Please make a folder for the project and put in there the code and this file.
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 README | 45 +++++++++++++++++++++++++++++++++++++++++++++
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 create mode 100644 README

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+System Requirements Document
+________________________________________
+1. Hyperparameter Tuning
+The system should provide the ability to fine-tune the hyperparameters of the neural network, enabling researchers and developers to optimize performance. Specific requirements include:
+1.1 Adjustable Hyperparameters:
+•	Number of Hidden Layers: Users must be able to increase or decrease the number of hidden layers to experiment with model depth.
+•	Number of Hidden Units: Each hidden layer should allow for customization of the number of units (neurons).
+•	Activation Functions: The system should support multiple activation functions such as ReLU, sigmoid, and tanh, selectable per layer.
+•	Learning Rate: Users must be able to specify a learning rate for the optimizer, with support for both constant and adaptive learning rates.
+•	Number of Epochs: The training process should allow the specification of the number of epochs, with visual feedback on progress.
+1.2 Configuration Management:
+•	A dedicated configuration file or graphical interface should be available to make changes to these parameters easily.
+•	The system should maintain a history of configurations for reproducibility and comparison of experiments.
+________________________________________
+2. Validation Mechanism
+To ensure the robustness of the model, the system must include a thorough validation mechanism with the following capabilities:
+2.1 Misclassification Detection:
+•	The system should automatically identify misclassified examples during the validation phase.
+•	For each class, at least one misclassified instance must be displayed, accompanied by the predicted label and true label.
+2.2 Visualization:
+•	The misclassified examples should be presented in a clear and concise format, such as images with overlaid labels or tabular summaries for non-visual data.
+•	Users should be able to export this information for further analysis.
+2.3 Metrics:
+•	The system should calculate and display common evaluation metrics, including accuracy, precision, recall, and F1-score, for all classes.
+________________________________________
+3. Workflow Improvements
+In addition to hyperparameter tuning, the system should support advanced workflow features to improve the overall accuracy of the neural network. These features include:
+3.1 Data Preprocessing:
+•	Support for advanced preprocessing techniques, such as normalization, standardization, and handling of missing values.
+•	Options for data augmentation, including rotation, scaling, cropping, and flipping of images, to increase training data diversity.
+3.2 Regularization:
+•	Implementation of regularization techniques such as dropout to prevent overfitting.
+•	Support for L1 and L2 regularization to control model complexity.
+3.3 Alternative Architectures:
+•	The system should provide templates or guidance for testing alternative architectures, such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs), depending on the dataset.
+3.4 Automated Suggestions:
+•	Incorporation of automated tools to recommend potential improvements, such as underutilized features or patterns in validation results.
+3.5 Logging and Monitoring:
+•	Continuous logging of training and validation metrics for detailed performance tracking.
+•	Real-time monitoring of resource utilization, including GPU/CPU usage, memory, and training time.
+________________________________________
+Additional Notes
+The system must be user-friendly and include comprehensive documentation for all features. It should also be designed to accommodate future enhancements and be compatible with industry-standard frameworks such as TensorFlow or PyTorch.
+________________________________________
+