Acelere su Código Python con Numba y Cupy
Acelera tu código Python utilizando Numba y Cupy: compila y mueve a la GPU para optimizar el rendimiento en machine learning y ciencia de datos.
Optimiza la Velocidad de tu Código Python con Numba JIT Compiler
Acelera tu código Python utilizando Numba y los decoradores JIT para optimizar el rendimiento y mejorar la eficiencia computacional.
Tools for High Performance Python - Ian Ozsvald - ODSC Europe 2019
Discover techniques for optimizing Python performance, including efficient Pandas operations, Numba compilation, and Dask parallelization. Learn to identify bottlenecks and implement solutions for faster data processing and analysis.
RAPIDS - The Platform Inside and Outside - Joshua Patterson - ODSC East 2019
Accelerating data science with GPUs using RAPIDS: open-source libraries for GPU-powered data manipulation, machine learning, and graph analytics, scaling across multiple GPUs and integrating with existing Python ecosystems.
Histogram-based Gradient Boosting in scikit-learn 0.21
Explore the new Histogram-based Gradient Boosting implementation in scikit-learn 0.21, offering competitive performance with XGBoost and LightGBM for efficient machine learning on tabular data.
Power Up Your Work with Compiling and Profiling
Discover how to optimize Python code performance using Numba, a JIT compiler for numerical calculations. Learn when and how to apply Numba effectively to speed up math-heavy code in data science and development projects.
Scientific Visualization with GR
Explore scientific visualization with GR framework, covering 2D graphs to 3D scenes. Learn integration techniques, performance optimization, and real-world applications in this comprehensive overview.
Numba - A JIT Compiler for Fast Numerical Code
Explore Numba, a JIT compiler for Python that accelerates numerical algorithms beyond Numpy capabilities, with CPU and GPU support. Learn its use cases, performance expectations, and inner workings.
Developing a Real-Time Automated Trading Platform with Python
Aprenda a desarrollar una plataforma de trading automatizado en tiempo real utilizando Python, explorando sistemas multiagente, procesamiento de datos y visualización con herramientas como ZeroMQ, NumPy y PyQtGraph.
Understanding Numba - The Python and Numpy Compiler
Explore Numba, a JIT compiler for Python and Numpy, to accelerate numerical code. Learn implementation techniques, performance optimization, and compare with other high-performance computing options.
Making Pandas Fly
Optimize Pandas for larger datasets: compression techniques, faster operations, and alternatives like Dask and Vaex. Learn to process more data efficiently and boost your data analysis capabilities.
Accelerating Python Code with Numba and CuPy for Machine Learning
Boost Python code execution with GPU acceleration and compilation using Numba and Cupy for machine learning tasks.
Optimizing Python Code Speed with Numba JIT Compiler
Accelerate Python code execution using Numba's JIT compiler, leveraging jit and njit decorators for enhanced performance.
Profile, Optimize, Repeat: One Core Is All You Need - Boosting Data Analysis Pipeline Performance
Boost data analysis pipeline performance on a single core. Learn profiling tools, optimization strategies, and techniques using numpy, numba jit-ing, and pybind11 extensions.
The Missing Python Introduction for Scientists - Performance and Efficiency
Explore Python's internals, performance optimization, and essential scientific libraries. Learn to write efficient code and choose the right tools for scientific computing tasks.
How to Make Python Faster: AOT, JIT and NoGIL Technologies
Explore AOT, JIT, and noGIL technologies to boost Python performance. Learn about Numba, mypyc, and GIL-free Python variants for faster application execution.