Search Results for "numerical"

DROP implements the model libraries and provides systems for fixed income valuation and adjustments, asset allocation and transaction cost analytics, and supporting libraries in numerical optimization and statistical learning. DROP is composed of four main libraries: [Asset Allocation] (https://lakshmidrip.github.io/DROP/AssetAllocationModule.html) [Fixed Income Analytics] (https://lakshmidrip.github.io/DROP/FixedIncomeModule.html) [Numerical Optimization] (https://lakshmidrip.github.io/DROP/NumericalOptimizerModule.html) [Statistical Learning] (https://lakshmidrip.github.io/DROP/StatisticalLearningModule.html)

...A data science layer adds NumPy-style reshaping and generators, Pandas-style numeric CSV, functional utils, and SciKit-Learn-style preprocessing (one-hot encoding, train/test split). An AI/ML layer adds activations, softmax, MSE loss, and numerical gradients for neural-network forward passes. An ultimate layer tops the scientific stack off with computational backend selection (CPU / AVX SIMD / GPU stub), a Micrograd-style scalar autograd engine.