Enables readers to develop foundational and advanced vectorization skills for scalable data science and machine learning and address real-world problemsOffering insights across various domains such as computer vision and natural language processing, Vectorization covers the fundamental topics of vectorization including array and tensor operations, data wrangling, and batch processing. This book illustrates how the principles discussed lead to successful outcomes in machine learning projects, serving as concrete examples for the theories explained, with each chapter including practical case studies and code implementations using NumPy, TensorFlow, and PyTorch.Each chapter has one or two types of contents: either an introduction comparison of the specific operations in the numerical libraries (illustrated as tables) and or case study examples that apply the concepts introduced to solve a practical problem (as code blocks and figures). Readers can approach the knowledge presented by reading the text description, running the code blocks, or examining the figures.

Vectorization: Machine Learning Algorithms PDF

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