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PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Condición: New.

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Paperback. Condición: new. Paperback. Are you frustrated by Python's performance limits when tackling large-scale simulations, data analysis, or machine learning tasks? Do you long for the speed of compiled languages without sacrificing Python's clarity?Advanced Python Scientific Computing: Profiling, Cython, Numba, and Distributed Analytics offers a practical roadmap to transform your code into a high-performance powerhouse. This book equips you with proven techniques-from pinpointing performance bottlenecks to harnessing GPUs and scaling across clusters-so you can tackle computationally intensive scientific workflows with confidence.What This Book DeliversProfiling Mastery: Identify time- and memory-critical hotspots using cProfile, line_profiler, memory_profiler, and tracemalloc. Learn to interpret profiling data and target your optimization efforts where they matter most.Cython Acceleration: Convert Python functions into C-level extensions. Understand setup tools, static typing, memoryviews, and interfacing with C libraries to boost loop-intensive code.Numba JIT and GPU Offloading: Write familiar Python while benefiting from LLVM-based just-in-time compilation. Compare atnjit and atjit, leverage prange for multicore parallelism, and deploy atcuda.jit kernels for GPU-accelerated computations.Distributed Analytics with Dask and MPI4Py: Scale NumPy and Pandas patterns to clusters. Build Dask task graphs, configure schedulers, tune performance with chunking and data locality, and integrate MPI4Py for hybrid HPC workflows.Modern Toolchain Integration: Port array code to CuPy, explore automatic differentiation and JIT compilation with JAX, and manage large datasets using HDF5, Zarr, and Apache Arrow.Why You'll BenefitTransform slow prototypes into production-ready, performance-optimized applications.Gain hands-on experience with industry-standard tools for profiling, JIT compilation, and distributed computing.Achieve near-C performance in Python, tap GPU power, and run analytics on clusters or in serverless environments.Build resilient, reproducible workflows with best practices in environment management, testing, and CI/CD.Ready to elevate your Python scientific computing skills and accelerate your research or production pipelines? Add Advanced Python Scientific Computing to your library today and start writing code that's both clear and lightning-fast. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …

Paperback. Condición: new. Paperback. Are you ready to conquer the complexities of TensorFlow and deliver realworld machine learning systems that scale? Many developers struggle to move beyond proof-of-concepts-models that work on a laptop but buckle under production demands.TensorFlow Machine Learning Tutorial for Developers provides the step-by-step playbook you need to build, optimize, and deploy production-ready models with confidence. From crafting high-throughput data pipelines and writing custom training loops with tf.GradientTape to applying mixed-precision, pruning, and quantization, this book transforms theory into practice. You'll master end-to-end workflows-containerized training on GPUs, serving with TensorFlow Serving or FastAPI, autoscaling in Kubernetes, serverless inference on Cloud Functions, and continuous retraining with TFX.What you'll achieve: Efficient Data Engineering: Implement tf.data pipelines that load, augment, and shard data for multi-GPU/TPU training.Advanced Model Development: Build models using tf.keras Sequential and Functional APIs, custom layers, attention blocks, and transfer learning modules.Performance Tuning: Apply mixed-precision training, XLA compilation, and distribution strategies to accelerate throughput.Edge and Cloud Deployment: Package models in Docker, deploy on Kubernetes with autoscaling, or host serverless TFLite microservices on AWS Lambda and Cloud Run.Robust MLOps Practices: Set up monitoring with Prometheus, Grafana, TensorBoard, detect data drift with TFDV, and automate CI/CD via GitHub Actions.Are you curious how to scale a Transformer-based chatbot, run inference on a microcontroller, or automate a TFX pipeline that retrains itself? This hands-on guide delivers complete, ready-to-run code examples-no fluff, no theory overload.Take the next step to transform your machine learning skills into production excellence. Grab your copy of TensorFlow Machine Learning Tutorial for Developers today and start building models that perform, scale, and endure. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condición: new. Paperback. Are you frustrated by Python's performance limits when tackling large-scale simulations, data analysis, or machine learning tasks? Do you long for the speed of compiled languages without sacrificing Python's clarity?Advanced Python Scientific Computing: Profiling, Cython, Numba, and Distributed Analytics offers a practical roadmap to transform your code into a high-performance powerhouse. This book equips you with proven techniques-from pinpointing performance bottlenecks to harnessing GPUs and scaling across clusters-so you can tackle computationally intensive scientific workflows with confidence.What This Book DeliversProfiling Mastery: Identify time- and memory-critical hotspots using cProfile, line_profiler, memory_profiler, and tracemalloc. Learn to interpret profiling data and target your optimization efforts where they matter most.Cython Acceleration: Convert Python functions into C-level extensions. Understand setup tools, static typing, memoryviews, and interfacing with C libraries to boost loop-intensive code.Numba JIT and GPU Offloading: Write familiar Python while benefiting from LLVM-based just-in-time compilation. Compare atnjit and atjit, leverage prange for multicore parallelism, and deploy atcuda.jit kernels for GPU-accelerated computations.Distributed Analytics with Dask and MPI4Py: Scale NumPy and Pandas patterns to clusters. Build Dask task graphs, configure schedulers, tune performance with chunking and data locality, and integrate MPI4Py for hybrid HPC workflows.Modern Toolchain Integration: Port array code to CuPy, explore automatic differentiation and JIT compilation with JAX, and manage large datasets using HDF5, Zarr, and Apache Arrow.Why You'll BenefitTransform slow prototypes into production-ready, performance-optimized applications.Gain hands-on experience with industry-standard tools for profiling, JIT compilation, and distributed computing.Achieve near-C performance in Python, tap GPU power, and run analytics on clusters or in serverless environments.Build resilient, reproducible workflows with best practices in environment management, testing, and CI/CD.Ready to elevate your Python scientific computing skills and accelerate your research or production pipelines? Add Advanced Python Scientific Computing to your library today and start writing code that's both clear and lightning-fast. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. …

Paperback. Condición: new. Paperback. Are you ready to conquer the complexities of TensorFlow and deliver realworld machine learning systems that scale? Many developers struggle to move beyond proof-of-concepts-models that work on a laptop but buckle under production demands.TensorFlow Machine Learning Tutorial for Developers provides the step-by-step playbook you need to build, optimize, and deploy production-ready models with confidence. From crafting high-throughput data pipelines and writing custom training loops with tf.GradientTape to applying mixed-precision, pruning, and quantization, this book transforms theory into practice. You'll master end-to-end workflows-containerized training on GPUs, serving with TensorFlow Serving or FastAPI, autoscaling in Kubernetes, serverless inference on Cloud Functions, and continuous retraining with TFX.What you'll achieve: Efficient Data Engineering: Implement tf.data pipelines that load, augment, and shard data for multi-GPU/TPU training.Advanced Model Development: Build models using tf.keras Sequential and Functional APIs, custom layers, attention blocks, and transfer learning modules.Performance Tuning: Apply mixed-precision training, XLA compilation, and distribution strategies to accelerate throughput.Edge and Cloud Deployment: Package models in Docker, deploy on Kubernetes with autoscaling, or host serverless TFLite microservices on AWS Lambda and Cloud Run.Robust MLOps Practices: Set up monitoring with Prometheus, Grafana, TensorBoard, detect data drift with TFDV, and automate CI/CD via GitHub Actions.Are you curious how to scale a Transformer-based chatbot, run inference on a microcontroller, or automate a TFX pipeline that retrains itself? This hands-on guide delivers complete, ready-to-run code examples-no fluff, no theory overload.Take the next step to transform your machine learning skills into production excellence. Grab your copy of TensorFlow Machine Learning Tutorial for Developers today and start building models that perform, scale, and endure. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…