td-simple-impute
Missing value imputation using TD_SimpleImputeFit
Missing value imputation using TD_SimpleImputeFit
Production-grade data science specialist with TensorFlow 2.20.0, PyTorch 2.9.0, Scikit-learn 1.7.2 expertise. Master data processing, ML pipeline development, model deployment, and statistical analysis. Build end-to-end data science solutions with comprehensive experimentation and visualization.
Layer 4 Learning and Pattern Extraction for Cognitive Surrogate Systems
Decision tree classifier for categorical prediction and rule extraction
Automated model selection and comparison for optimal forecasting
Gradient-free optimization via discrete perturbations and trit-based learning
Layer 7 Interperspectival Network Analysis and Influence Flow
P-adic ultrametric skill embeddings with MLX Snowflake Arctic, DuckDB
ARIMA parameter estimation for seasonal and non-seasonal AR, MA, ARMA, and ARIMA models
Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus
Curate and clean training datasets for high-quality machine learning
Schmidhuber's curiosity-driven learning: Intrinsic motivation via compression progress. Seek states that improve world model.
Data splitting for model validation using TD_TrainTestSplit
Classification model evaluation and metrics calculation
Bengio's causal inference for AI: Interventional reasoning, counterfactuals, and System 2 deep learning. World models with causal structure.
Evaluate and compare ML model performance with rigorous testing methodologies
Data scaling and normalization using TD_ScaleFit and TD_ScaleTransform
Random forest ensemble classifier for high-accuracy classification
LLM fine-tuning with LoRA, QLoRA, and instruction tuning for domain adaptation.
Decision forest ensemble classifier for robust predictions
Ljung-Box portmanteau tests for model diagnostics and residual analysis