Items related to Google JAX Cookbook: Perform machine learning and numerical...
Google JAX Cookbook: Perform machine learning and numerical computing with combined capabilities of TensorFlow and NumPy - Softcover
- PublisherGitforGits
- Publication date2024
- ISBN 10 8197950415
- ISBN 13 9788197950414
- BindingPaperback
- LanguageEnglish
- Number of pages250
Search results for Google JAX Cookbook: Perform machine learning and numerical...
Stock Image
Google JAX Cookbook: Perform machine learning and numerical computing with combined capabilities of TensorFlow and NumPy
Seller: California Books, Miami, FL, U.S.A.
Seller rating 5 out of 5 stars
Condition: New. Seller Inventory # I-9788197950414
Quantity: Over 20 available
Stock Image
Google JAX Cookbook (Paperback)
Seller: Grand Eagle Retail, Fairfield, OH, U.S.A.
Seller rating 5 out of 5 stars
Paperback. Condition: new. Paperback. 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.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. The debugging section provides clear and effective strategies for resolving common runtime issues, including shape mismatches, NaNs, and control flow errors. The book goes on to show you how to master Pytrees for data manipulation, integrate external functions through the Foreign Function Interface (FFI), and utilize advanced serialization and type promotion techniques for stable computations.If you want to optimize training processes, this book has you covered. It includes recipes for efficient data loading, building custom neural networks, implementing mixed precision, and tracking experiments with Penzai. You'll learn how to visualize model performance and monitor metrics to assess training progress effectively. The recipes in this book tackle real-world scenarios and give users the power to fix issues and fine-tune models quickly.Key LearningsGet your calculations done faster by moving from NumPy to JAX's optimized framework.Make your training pipelines more efficient by profiling how long things take and how much memory they use.Use debugging techniques to fix runtime issues like shape mismatches and numerical instability.Get to grips with Pytrees for managing complex, nested data structures across various machine learning tasks.Use JAX's Foreign Function Interface (FFI) to bring in external functions and give your computational capabilities a boost.Take advantage of mixed-precision training to speed up neural network computations without sacrificing model accuracy.Keep your experiments on track with Penzai. This lets you reproduce results and monitor key metrics.Create your own neural networks and optimizers directly in JAX so you have full control of the architecture.Use serialization techniques to save, load, and transfer models and training checkpoints efficiently.Table of ContentTransition NumPy to JAXProfiling Computation and Device MemoryDebugging Runtime Values and ErrorsMastering Pytrees for Data StructuresExporting and SerializationType Promotion Semantics and Mixed PrecisionIntegrating Foreign Functions (FFI)Training Neural Networks with JAX Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9788197950414
Quantity: 1 available
Stock Image
Google JAX Cookbook: Perform machine learning and numerical computing with combined capabilities of TensorFlow and NumPy
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In. Seller Inventory # ria9788197950414_new
Quantity: Over 20 available
Seller Image
Google JAX Cookbook
Print on DemandSeller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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.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. The debugging section provides clear and effective strategies for resolving common runtime issues, including shape mismatches, NaNs, and control flow errors. The book goes on to show you how to master Pytrees for data manipulation, integrate external functions through the Foreign Function Interface (FFI), and utilize advanced serialization and type promotion techniques for stable computations.If you want to optimize training processes, this book has you covered. It includes recipes for efficient data loading, building custom neural networks, implementing mixed precision, and tracking experiments with Penzai. You'll learn how to visualize model performance and monitor metrics to assess training progress effectively. The recipes in this book tackle real-world scenarios and give users the power to fix issues and fine-tune models quickly.Key LearningsGet your calculations done faster by moving from NumPy to JAX's optimized framework.Make your training pipelines more efficient by profiling how long things take and how much memory they use.Use debugging techniques to fix runtime issues like shape mismatches and numerical instability.Get to grips with Pytrees for managing complex, nested data structures across various machine learning tasks.Use JAX's Foreign Function Interface (FFI) to bring in external functions and give your computational capabilities a boost.Take advantage of mixed-precision training to speed up neural network computations without sacrificing model accuracy.Keep your experiments on track with Penzai. This lets you reproduce results and monitor key metrics.Create your own neural networks and optimizers directly in JAX so you have full control of the architecture.Use serialization techniques to save, load, and transfer models and training checkpoints efficiently.Table of ContentTransition NumPy to JAXProfiling Computation and Device MemoryDebugging Runtime Values and ErrorsMastering Pytrees for Data StructuresExporting and SerializationType Promotion Semantics and Mixed PrecisionIntegrating Foreign Functions (FFI)Training Neural Networks with JAX 252 pp. Englisch. Seller Inventory # 9788197950414
Quantity: 2 available
Stock Image
Google JAX Cookbook (Paperback)
Seller: AussieBookSeller, Truganina, VIC, Australia
Seller rating 3 out of 5 stars
Paperback. Condition: new. Paperback. 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.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. The debugging section provides clear and effective strategies for resolving common runtime issues, including shape mismatches, NaNs, and control flow errors. The book goes on to show you how to master Pytrees for data manipulation, integrate external functions through the Foreign Function Interface (FFI), and utilize advanced serialization and type promotion techniques for stable computations.If you want to optimize training processes, this book has you covered. It includes recipes for efficient data loading, building custom neural networks, implementing mixed precision, and tracking experiments with Penzai. You'll learn how to visualize model performance and monitor metrics to assess training progress effectively. The recipes in this book tackle real-world scenarios and give users the power to fix issues and fine-tune models quickly.Key LearningsGet your calculations done faster by moving from NumPy to JAX's optimized framework.Make your training pipelines more efficient by profiling how long things take and how much memory they use.Use debugging techniques to fix runtime issues like shape mismatches and numerical instability.Get to grips with Pytrees for managing complex, nested data structures across various machine learning tasks.Use JAX's Foreign Function Interface (FFI) to bring in external functions and give your computational capabilities a boost.Take advantage of mixed-precision training to speed up neural network computations without sacrificing model accuracy.Keep your experiments on track with Penzai. This lets you reproduce results and monitor key metrics.Create your own neural networks and optimizers directly in JAX so you have full control of the architecture.Use serialization techniques to save, load, and transfer models and training checkpoints efficiently.Table of ContentTransition NumPy to JAXProfiling Computation and Device MemoryDebugging Runtime Values and ErrorsMastering Pytrees for Data StructuresExporting and SerializationType Promotion Semantics and Mixed PrecisionIntegrating Foreign Functions (FFI)Training Neural Networks with JAX Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Seller Inventory # 9788197950414
Quantity: 1 available
Seller Image
Google JAX Cookbook : Perform machine learning and numerical computing with combined capabilities of TensorFlow and NumPy
Print on DemandSeller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.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. The debugging section provides clear and effective strategies for resolving common runtime issues, including shape mismatches, NaNs, and control flow errors. The book goes on to show you how to master Pytrees for data manipulation, integrate external functions through the Foreign Function Interface (FFI), and utilize advanced serialization and type promotion techniques for stable computations.If you want to optimize training processes, this book has you covered. It includes recipes for efficient data loading, building custom neural networks, implementing mixed precision, and tracking experiments with Penzai. You'll learn how to visualize model performance and monitor metrics to assess training progress effectively. The recipes in this book tackle real-world scenarios and give users the power to fix issues and fine-tune models quickly.Key LearningsGet your calculations done faster by moving from NumPy to JAX's optimized framework.Make your training pipelines more efficient by profiling how long things take and how much memory they use.Use debugging techniques to fix runtime issues like shape mismatches and numerical instability.Get to grips with Pytrees for managing complex, nested data structures across various machine learning tasks.Use JAX's Foreign Function Interface (FFI) to bring in external functions and give your computational capabilities a boost.Take advantage of mixed-precision training to speed up neural network computations without sacrificing model accuracy.Keep your experiments on track with Penzai. This lets you reproduce results and monitor key metrics.Create your own neural networks and optimizers directly in JAX so you have full control of the architecture.Use serialization techniques to save, load, and transfer models and training checkpoints efficiently.Table of ContentTransition NumPy to JAXProfiling Computation and Device MemoryDebugging Runtime Values and ErrorsMastering Pytrees for Data StructuresExporting and SerializationType Promotion Semantics and Mixed PrecisionIntegrating Foreign Functions (FFI)Training Neural Networks with JAX. Seller Inventory # 9788197950414
Quantity: 2 available
Stock Image
Google JAX Cookbook (Paperback)
Seller: CitiRetail, Stevenage, United Kingdom
Seller rating 5 out of 5 stars
Paperback. Condition: new. Paperback. 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.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. The debugging section provides clear and effective strategies for resolving common runtime issues, including shape mismatches, NaNs, and control flow errors. The book goes on to show you how to master Pytrees for data manipulation, integrate external functions through the Foreign Function Interface (FFI), and utilize advanced serialization and type promotion techniques for stable computations.If you want to optimize training processes, this book has you covered. It includes recipes for efficient data loading, building custom neural networks, implementing mixed precision, and tracking experiments with Penzai. You'll learn how to visualize model performance and monitor metrics to assess training progress effectively. The recipes in this book tackle real-world scenarios and give users the power to fix issues and fine-tune models quickly.Key LearningsGet your calculations done faster by moving from NumPy to JAX's optimized framework.Make your training pipelines more efficient by profiling how long things take and how much memory they use.Use debugging techniques to fix runtime issues like shape mismatches and numerical instability.Get to grips with Pytrees for managing complex, nested data structures across various machine learning tasks.Use JAX's Foreign Function Interface (FFI) to bring in external functions and give your computational capabilities a boost.Take advantage of mixed-precision training to speed up neural network computations without sacrificing model accuracy.Keep your experiments on track with Penzai. This lets you reproduce results and monitor key metrics.Create your own neural networks and optimizers directly in JAX so you have full control of the architecture.Use serialization techniques to save, load, and transfer models and training checkpoints efficiently.Table of ContentTransition NumPy to JAXProfiling Computation and Device MemoryDebugging Runtime Values and ErrorsMastering Pytrees for Data StructuresExporting and SerializationType Promotion Semantics and Mixed PrecisionIntegrating Foreign Functions (FFI)Training Neural Networks with JAX Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9788197950414
Quantity: 1 available