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Projects/README.md

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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.