Mcadams axel (2 Ergebnisse)

Taschenbuch. Zustand: Neu. Neuware - Build, tune, explain, scale, and deploy modern XGBoost models with confidence.XGBoost can deliver powerful results on tabular data, but effective machine learning requires much more than fitting a classifier or adjusting a few hyperparameters. You need to understand objectives, gradients, Hessians, validation, categorical data, ranking, explainability, performance, distributed training, and the production decisions that determine whether a model remains reliable after deployment.This practical guide takes you from the foundations of gradient boosted trees through modern XGBoost workflows for classification, regression, learning to rank, GPU acceleration, Apache Spark, and production machine learning systems. Along the way, you will learn not only how to configure models, but how to diagnose why they succeed, fail, overfit, slow down, drift, or behave unexpectedly.What you will learn: - Understand objectives gradients Hessians leaf weights split gain and histogram based tree construction- Prepare NumPy pandas Polars PyArrow sparse and categorical data correctly while preventing leakage- Build binary multiclass and multi label classifiers with appropriate metrics thresholds imbalance handling and calibration- Apply squared error robust Poisson Gamma Tweedie quantile expectile and multi output regression- Train learning to rank models with LambdaRank LambdaMART NDCG MAP query groups pair construction and click bias handling- Tune learning rate tree complexity sampling and regularization with early stopping cross validation random search successive halving and Optuna- Diagnose underfitting overfitting instability memory pressure latency and inefficient parallelism- Interpret predictions with feature importance TreeSHAP waterfall beeswarm dependence cohort and interaction analysis- Use monotonic constraints interaction constraints DART custom objectives and vector leaf multi output modeling- Accelerate training with CUDA QuantileDMatrix external memory adaptive caching and GPU aware data pipelines- Scale training with Spark workers partitions GPU allocation ranking validation distributed SHAP and model persistence- Build production workflows with JSON and UBJSON serialization schema validation feature contracts drift monitoring reproducibility retraining and rollback planningThe guide includes extensive Python code throughout, showing how the concepts translate into practical model training, evaluation, tuning, explanation, benchmarking, distributed execution, and production workflows you can adapt to real projects.If you want to move beyond basic boosting tutorials and develop XGBoost systems that are accurate, explainable, scalable, and production ready, grab your copy today.…

Taschenbuch. Zustand: Neu. Neuware - Build a complete long range sensor monitoring system that connects Raspberry Pi 5 gateways, ESP32 sensor nodes, LoRaWAN networking, local automation, and dependable remote monitoring.Getting a LoRaWAN project working on a desk is one thing. Designing a system that joins reliably, preserves sessions, survives power loss, uses radio airtime efficiently, operates from batteries or solar power, and continues collecting data when internet access fails requires a much deeper understanding.This practical guide takes you from radio fundamentals and gateway design through sensor-node firmware, environmental monitoring, automation, storage, troubleshooting, and production deployment. You will learn how the pieces fit together and how to avoid common mistakes that lead to failed joins, weak coverage, excessive battery drain, unreliable downlinks, and difficult field maintenance.- Understand LoRa modulation, spreading factors, bandwidth, coding rate, link budgets, regional parameters, and LoRaWAN device classes- Design a Raspberry Pi 5 gateway using SX1302 or SX1303 concentrator hardware, suitable antennas, reliable power, cooling, and storage- Install and configure ChirpStack, MQTT communication, gateway forwarding, persistent services, and secure remote access- Build ESP32-S3 and SX1262 sensor nodes with RadioLib, OTAA activation, session persistence, deep sleep, retries, and power loss recovery- Create compact binary payloads with scaling, flags, sequence numbers, schema versions, and ChirpStack JavaScript codecs- Use ADR, spreading factors, transmit power, uplink timing, confirmed traffic, and safe downlink commands efficiently- Build temperature, humidity, soil moisture, tank level, leak, gate, door, rainfall, wind, and multi-sensor monitoring nodes- Measure real power consumption and design battery and solar systems with reserve capacity, charging limits, and energy-aware firmware- Prepare outdoor nodes with weather protection, venting, cable glands, condensation control, UV resistance, and service access- Connect MQTT, Node-RED, InfluxDB, and Grafana for telemetry, alerts, automation, historical storage, and visualization- Design offline-tolerant monitoring with local storage, WAN recovery, durable event buffering, and VPN-based administration- Test RF coverage with RSSI, SNR, packet error rate, terrain, antennas, and ADR while diagnosing join, uplink, downlink, sensor, and network faults- Scale toward multiple gateways and sensor fleets with redundancy, provisioning, onboarding, relay concepts, watchdogs, backups, and deployment acceptance testingPractical C++, JavaScript, Python, Shell, configuration, payload codec, MQTT, automation, persistence, and monitoring examples help turn the concepts into real gateway and sensor-node implementations.Grab your copy today and build a LoRaWAN monitoring system designed for reliable field operation, not just a successful bench test.…