| Aspect | Feature Selection | Feature Engineering |
| Purpose | Choose relevant features | Create informative features |
| Objective | Improve model performance by eliminating irrelevant or redundant features | Improve model's ability to capture patterns and relationships |
| Techniques | Correlation analysis, mutual information, feature importance scores, recursive feature elimination | Scaling, one-hot encoding, interaction terms, mathematical operations, date-related feature extraction |
| Automation | Can often be automated using statistical methods and algorithms | May require domain knowledge and human expertise |
| Examples | SelectKBest, SelectFromModel, Recursive Feature Elimination (RFE) | One-hot encoding, polynomial feature creation, date-related feature extraction |
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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