| Technique | Description | Real-Life Example |
| Resampling | - Oversampling: Increase the number of minority class samples. - Undersampling: Reduce the number of majority class samples. | Example: In fraud detection, where fraudulent transactions are rare, you can oversample the minority class to balance the dataset. Conversely, you can undersample non-fraudulent transactions. |
| Synthetic Data | Generate synthetic samples for the minority class using techniques like SMOTE (Synthetic Minority Over-sampling Technique). | Example: In medical diagnosis, when positive cases are scarce, generate synthetic data points to improve model accuracy. |
| Cost-Sensitive Learning | Modify the algorithm's objective function to penalize misclassification of the minority class more than the majority class. | Example: In healthcare, misdiagnosing a rare disease may be costlier, so the algorithm can be tuned to minimize such errors. |
| Ensemble Methods | Combine predictions from multiple models to improve performance, e.g., Random Forests, AdaBoost, or XGBoost. | Example: In credit scoring, ensemble methods can help balance recall and precision when dealing with rare default cases. |
| Anomaly Detection | Treat the minority class as anomalies and use anomaly detection algorithms like Isolation Forest or One-Class SVM. | Example: In network security, detecting rare intrusions among legitimate traffic patterns. |
| Change the Threshold | Adjust the classification threshold to increase sensitivity or specificity based on the problem's requirements. | Example: In email spam detection, lowering the threshold may increase the recall of spam emails. |
| Collect More Data | Sometimes, collecting more data for the minority class may be a practical solution if feasible. | Example: In manufacturing, if defective products are rare, collecting more data on defect cases can help. |
Tensor Parallelism in GPU Tensor parallelism is a technique used to distribute the computation of large tensor operations across multiple GPUs or multiple cores within a GPU . It is an essential method for improving the performance and scalability of deep learning models, particularly when dealing with very large models that cannot fit into the memory of a single GPU. Key Concepts Tensor Operations : Tensors are multidimensional arrays used extensively in deep learning. Common tensor operations include matrix multiplication, convolution, and element-wise operations. Parallelism : Parallelism involves dividing a task into smaller sub-tasks that can be executed simultaneously. This approach leverages the parallel processing capabilities of GPUs to speed up computations. How Tensor Parallelism Works Splitting Tensors : The core idea of tensor parallelism is to split large tensors into smaller chunks that can be processed in parallel. Each chunk is assigned to a different GP...
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