High-Performance Data Analysis with PyTorch Accelerate Pandas Workflows and Build GPU-Powered Statistical Models [#1017942]

High-Performance Data Analysis with PyTorch: Accelerate Pandas Workflows and Build GPU-Powered Statistical Models
English | January 6, 2026 | ASIN: B0GF84NQ1H | 371 pages | Epub | 834.30 KB
High-Performance Data Analysis with PyTorch is a practical, systems-level guide to using PyTorch as a fast, scalable engine for modern data analysis-not just deep learning. The book shows how to move beyond CPU-bound workflows and traditional tools by exploiting GPUs, vectorized tensor math, and automatic differentiation to analyze massive tabular datasets efficiently, all while staying entirely in Python. Starting from first principles, the book reframes tensors as a direct, powerful replacement for familiar Pandas and NumPy abstractions. Readers learn how precision choices, memory layouts, and device placement directly impact performance, and how to move data between CPU and GPU without bottlenecks. Core concepts like broadcasting, vectorization, and the computational graph are introduced from a data-analysis perspective, enabling loop-free computations and gradient-based reasoning for statistics, optimization, and sensitivity analysis. The book then dives into real-world performance engineering. It covers zero-copy data loading, GPU-accelerated preprocessing, and scalable data cleaning techniques that operate on millions of rows in milliseconds. Classical statistical methods-regression, classification, regularization, and optimization-are rebuilt using PyTorch's linear algebra and autograd, allowing workloads that overwhelm traditional libraries to run efficiently on modern hardware. Moving beyond classical methods, the book demonstrates how deep learning unlocks new capabilities for tabular data, including entity embeddings, mixed-type models, uncertainty estimation, and probabilistic analysis. Unsupervised techniques such as clustering and autoencoders are explored as tools for anomaly detection and dimensionality reduction at scale. Finally, the book addresses deployment and production concerns: integrating PyTorch models into familiar Scikit-Learn workflows, exporting models for high-performance inference, and serving predictions efficiently through batched, GPU-aware pipelines. Throughout, the emphasis is on clarity, performance intuition, and practical decision-making. Rather than treating PyTorch as a black box for neural networks, this book positions it as a unified, high-performance foundation for data analysis, statistics, and machine learning-designed for analysts, data scientists, and engineers who need speed, scale, and control without leaving Python.