Artículo: AMZ-B0FBSF5PRN

Efficient R Programming: High-Performance Data.table, Rcpp, and Parallel Computing (Machine Learning Mastery Toolkit)

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Peso con empaque
0.20 kg
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No
Condición
Nuevo
Producto de
Amazon

Sobre este producto
  • Fundamentals of Efficient R Coding: Learn why vectorization, memory pre-allocation, and built-in functions save seconds or minutes on every run.
  • Mastering data.table: Explore keys, fast joins, rolling operations, and internal indexing to handle millions of rows instantly.
  • Profiling and Benchmarking Techniques: Use microbenchmark, bench, profvis, and flame graphs to pinpoint slow code and track improvements.
  • Parallel Computing in R: Discover mclapply, parLapply, futures, foreach, and GPU frameworks to distribute workloads across cores and devices.
  • Rcpp for Speed and Scalability: Write inline C++ functions, leverage RcppEigen and RcppArmadillo for matrix algebra, and debug compiled code with minimal fuss.
  • GPU Computing and Accelerated Workflows: Apply gpuR and ArrayFire for image processing, deep learning, and custom OpenCL kernels to push performance to the hardware limit.
  • Distributed Computing with Spark: Translate data.table pipelines into sparklyr workflows for terabyte-scale data, Spark SQL, and MLlib models.
  • Cross-Language Interoperability and APIs: Call Python via reticulate, serve R logic as REST APIs with plumber, orchestrate multi-language pipelines using targets, and containerize services with Docker and Kubernetes.
  • Building High-Performance R Packages: Structure packages for compiled code, automate tests and benchmarks in CI, navigate CRAN checks, and manage vignettes effectively.
  • Real-World Case Studies: See how leading teams apply these strategies in high-frequency finance, genomics, and web-scale Shiny applications.

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