Requirements: .ePUB, .PDF, .MOBI/.AZW reader, 10 MB
Overview: This is the practical, solution-oriented book for every data scientists, Machine Learning engineers, and AI engineers to utilize the most of Google JAX for efficient and advanced Machine Learning. It covers essential tasks, troubleshooting scenarios, and optimization techniques to address common challenges encountered while working with JAX across Machine Learning and numerical computing projects. JAX is a Python library for accelerator-oriented array computation and program transformation, designed for high-performance numerical computing and large-scale Machine Learning. JAX provides a familiar NumPy-style API for ease of adoption by researchers and engineers. The book starts with the move from NumPy to JAX. It introduces the best ways to speed up computations, handle data types, generate random numbers, and perform in-place operations. It then shows you how to use profiling techniques to monitor computation time and device memory, helping you to optimize training and performance.
Genre: Non-Fiction > Tech & Devices
Download Instructions:
https://ouo.io/a5f73K
https://ouo.io/EM20UQ
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