Tabla de Contenidos: “…封 -- 版权声明 -- 内容提 -- 作 简介 -- 前 -- 审稿人简介 -- 目录 -- 第一 分 导 --熟悉实时分析 -- 第1章 实时分析简介 -- 1.1 大数据的定义 -- 1.2 大数据的基础 施 -- 1.3 实时分析--神 与现实 -- 1.4 实时 决方案--可用的架构 -- 1.4.1 NRT的Storm 决方案 -- 1.4.2 NRT的Spark 决方案 -- 1.5 Lambda 架构--分析可 性 -- 1.6 物 网--想法与可 性 -- 缘分析 -- 1.7 云-- NRT 和物 网 -- 1.8 小结 -- 第2章 实时应用的基本组件 -- 2.1 NRT 系统及其构建模块 -- 2.1.1 数据 -- 2.1.2 流处理 -- 2.1.3 分析层-服务终端用户 -- 2.2 NRT 的 级系统 图 -- 2.3 NRT 的技术 图 -- 2.3.1 事件生产 -- 2.3.2 数据收 -- 2.3.3 代理 -- 2.3.4 换和处理 -- 2.3.5 存储 -- 2.4 小结 -- 第二 分 搭建基础 施 -- 第3章 了 和 数据流 -- 3.1 了 数据流 -- 3.2 为数据提取安 基础 施 -- 3.2.1 Apache Kafka -- 3.2.2 Apache NiFi -- 3.2.3 Logstash -- 3.2.4 Fluentd -- 3.2.5 Flume -- 3.3 将数据从源填到处理器--期望和注意事 -- 3.4 比 与 择 合用例的最佳实 -- 3.5 小 牛刀 -- 置Elasticsearch -- 3.6 小结 -- 第4章 安 和 置Storm -- 4.1 Storm 概 -- 4.2 Storm 架构和组件 -- 4.2.1 特征 -- 4.2.2 组件 -- 4.2.3 流分组 -- 4.3 安 和 置 Storm -- 4.3.1 安 ZooKeeper -- 4.3.2 置Apache Storm -- 4.4 在 Storm 上实时处理任务 -- 任务 -- 4.5 小结 -- 第5章 置Apache Spark和Flink -- 5.1 安 并快 Spark -- 5.1.1 源码构建 -- 5.1.2 下 Spark 安 包 -- 5.1.3 示例 -- 5.2 安 并快 Flink -- 5.2.1 使用源码构建Flink -- 5.2.2 下 Flink -- 5.2.3 示例 -- 5.3 安 并快 Apache Beam -- 5.3.1 Beam模型 -- 5.3.2 示例 -- 5.3.3 MinimalWordCount 示例 -- 5.4 Apache Beam 中的平 -- 5.5 小结 -- 第三 分 Storm 实时 算 -- 第6章 成Storm 与数据源 -- 6.1 RabbitMQ 有效的消息传 -- 6.2 RabbitMQ 交换器 -- 6.2.1 直接交换器 -- 6.2.2 RabbitMQ安 置 -- 6.2.3 RabbitMQ的发布和 -- 6.3 RabbitMQ 与 Storm 成 -- AMQPSpout -- 6.4 PubNub 数据流发布 -- 6.5 将Storm 和RMQ_PubNub 传感器数据拓扑串在一 -- 6.6 小结 -- 第7章 从Storm 到Sink -- 7.1 安 并 置 Cassandra -- 7.1.1 安 Cassandra -- 7.1.2 置Cassandra -- 7.2 Storm 和 Cassandra 拓扑 -- 7.3 Storm 和 IMDB 成处理维度数据 -- 7.4 成 示层与 Storm -- 使用Elasticsearch插件 置Grafana -- 7.5 小 牛刀 -- 7.6 小结 -- 第8章 Storm Trident -- 8.1 状态保持和 Trident -- 8.1.1 事务性spout -- 8.1.2 不 明事务性spout -- 8.2 基本 Storm Trident 拓扑 -- 8.3 Trident 内 实现…”

Tabla de Contenidos: “…Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- About the Editors -- Contributors -- Part I Secure AI/ML Systems: Attack Models -- 1 Machine Learning Attack Models -- 1.1 Introduction -- 1.2 Background -- 1.2.1 Notation -- 1.2.2 Support Vector Machines -- 1.2.3 Neural Networks -- 1.3 White-Box Adversarial Attacks -- 1.3.1 L-BGFS Attack -- 1.3.2 Fast Gradient Sign Method -- 1.3.3 Basic Iterative Method -- 1.3.4 DeepFool -- 1.3.5 Fast Adaptive Boundary Attack -- 1.3.6 Carlini and Wagner's Attack -- 1.3.7 Shadow Attack -- 1.3.8 Wasserstein Attack -- 1.4 Black-Box Adversarial Attacks -- 1.4.1 Transfer Attack -- 1.4.2 Score-Based Black-Box Attacks -- ZOO Attack -- Square Attack -- 1.4.3 Decision-Based Attack -- Boundary Attack -- HopSkipJump Attack -- Spatial Transformation Attack -- 1.5 Data Poisoning Attacks -- 1.5.1 Label Flipping Attacks -- 1.5.2 Clean Label Data Poisoning Attack -- Feature Collision Attack -- Convex Polytope Attack and Bullseye Polytope Attack -- 1.5.3 Backdoor Attack -- 1.6 Conclusions -- Acknowledgment -- Note -- References -- 2 Adversarial Machine Learning: A New Threat Paradigm for Next-Generation Wireless Communications -- 2.1 Introduction -- 2.1.1 Scope and Background -- 2.2 Adversarial Machine Learning -- 2.3 Challenges and Gaps -- 2.3.1 Development Environment -- 2.3.2 Training and Test Datasets -- 2.3.3 Repeatability, Hyperparameter Optimization, and Explainability -- 2.3.4 Embedded Implementation -- 2.4 Conclusions and Recommendations -- References -- 3 Threat of Adversarial Attacks to Deep Learning: A Survey -- 3.1 Introduction -- 3.2 Categories of Attacks -- 3.2.1 White-Box Attacks -- FGSM-based Method -- JSMA-based Method -- 3.2.2 Black-Box Attacks -- Mobility-based Approach -- Gradient Estimation-Based Approach -- 3.3 Attacks Overview…”

Tabla de Contenidos: “…Python problem representation -- Genetic algorithms solution -- Solving the graph coloring problem -- Solution representation -- Using hard and soft constraints for the graph coloring problem -- Python problem representation -- Genetic algorithms solution -- Summary -- Further reading -- Chapter 6: Optimizing Continuous Functions -- Technical requirements -- Chromosomes and genetic operators for real numbers -- Using DEAP with continuous functions -- Optimizing the Eggholder function -- Optimizing the Eggholder function with genetic algorithms -- Improving the speed with an increased mutation rate -- Optimizing Himmelblau's function -- Optimizing Himmelblau's function with genetic algorithms -- Using niching and sharing to find multiple solutions -- Simionescu's function and constrained optimization -- Constrained optimization with genetic algorithms -- Optimizing Simionescu's function using genetic algorithms -- Using constraints to find multiple solutions -- Summary -- Further reading -- Part 3: Artificial Intelligence Applications of Genetic Algorithms -- Chapter 7: Enhancing Machine Learning Models Using Feature Selection -- Technical requirements -- Supervised machine learning -- Classification -- Regression -- Supervised learning algorithms -- Feature selection in supervised learning -- Selecting the features for the Friedman-1 regression problem -- Solution representation -- Python problem representation -- Genetic algorithms solution -- Selecting the features for classifying the Zoo dataset -- Python problem representation -- Genetic algorithms solution -- Summary -- Further reading -- Chapter 8: Hyperparameter Tuning of Machine Learning Models -- Technical requirements -- Hyperparameters in machine learning -- Hyperparameter tuning -- The Wine dataset -- The adaptive boosting classifier…”

Tabla de Contenidos: “…EL PASADO SE SIENTA A LA MESA -- SS -- NATURALEZA MUERTA -- EL LIBRO DEL DESASOSIEGO -- CELAN, 1970 -- LA REALIDAD ES UN CUADRO DE VAN GOGH -- VOLVER A LA OSCURIDAD (BACK TO BLACK) -- MADAME GODOT -- ARP-PEGIOS -- ARRIVEDERCI CARONTE -- A PESSOA UNA SOLA PERSONALIDAD -- ATRAVIESO EL OLVIDO EN LA BARCA DE CARONTE -- EL CONSERJE DE UN HOTEL EN LLAMAS -- EN TIEMPOS DE ELECCIONES -- EL NUEVO INQUILINO -- RECADOS PARA MINERVA -- RETOCAR UN POEMA NO BASTA -- EL INFIERNO REVISITADO -- LAS UVAS DE LA IRA -- LA CLAVE ES EL MAÍZ -- LA SCALA DE MILÁN -- ESPEJO SIMBOLISTA -- EL SHOW DEBE CONTINUAR -- LA VERDAD TRAS EL MITO -- MÁS TEMPRANO QUE TARDE -- MOSCAS PREHISTÓRICAS / PREHISPÁNICAS / PREHISTÉRICAS -- EL ESPECTRO DE DALÍ -- NARCISO REVISITADO -- [QUÉ CARA PONDRÁ EL PAN -- VI LA SOGA DE JUDAS EN EL CUELLO DEL CISNE -- POSTALES DE ROMA -- A LA ORILLA DE LA NADA -- [LOS MUERTOS NO SON LO QUE PARECEN] -- CIRCO DE DIEZ PISTAS -- ZOO EN DIEZ JAULAS -- ENTERRANDO A PEREC -- [VI A DIOS DANDO CUERDA...] -- [VI A NERÓN EN EL LOBBY...] -- [VI A LA MADRE DE DUCHAMP...] -- [VI A CHARLES AZNAVOUR EN UNA GÓNDOLA VENECIANA...] -- [VI A BUKOWSKI OJEANDO LOS POEMAS DE DIOS...] -- [VI A NERUDA EN UNA PLAYA DEL AQUERONTE...] -- [VI A LA MUERTE CAMBIAR PAÑALES A DIOS...] -- POSTA -- [VI A DIOS EN EL ZOOLÓGICO DE PALO...] -- [VI A CHARLES MANSON...] -- [VI A DIOS CORTARSE LAS VENAS...] -- [VI A LA MUERTE ARROJAR -- EL PASADO OSCURO DE DIOS AC / DC -- QUIÉN ES QUIÉN -- EL PASADO OSCURO DE DIOS -- DE LOS EXTRAÑOS HECHOS QUE ACAECIERON EN EL CALVARIO -- DETALLES DE LA CREACIÓN -- EL ÚLTIMO DE LOS BEAT -- ACTA DE DEFUNCIÓN -- SUFRES DE ALUCINACIONES VIOLENTAS -- NOÉ ES INTERROGADO POR LA BRIGADA DE HOMICIDIOS -- ESA CRUZ EN LLAMAS NO ME PERTENECE -- TU VIDA ES UN LAMENTO CONTINUO -- EVANGELIOS APÓCRIFOS -- GRAFITIS HALLADOS EN EL MURO DE LOS LAMENTOS…”

Tabla de Contenidos: “…Analyzing missing data -- Combining data using a JOIN operation -- Munging textual data -- Processing multiple input data files -- Removing stop words -- Munging time series data -- Pre-processing of the&amp -- #160 -- time-series Dataset -- Processing date fields -- Persisting and loading data -- Defining a date-time index -- Using the&amp -- #160 -- &amp -- #160 -- TimeSeriesRDD&amp -- #160 -- object -- Handling missing time-series data -- Computing basic statistics -- Dealing with variable length records -- Converting variable-length records to fixed-length records -- Extracting data from "messy" columns -- Preparing data for machine learning -- Pre-processing data for machine learning -- Creating and running a machine learning pipeline -- Summary -- Chapter 5: Using Spark SQL in Streaming Applications -- Introducing streaming data applications -- Building Spark streaming applications -- Implementing sliding window-based functionality -- Joining a streaming Dataset with a static Dataset -- Using the Dataset API in Structured Streaming -- Using output sinks -- Using the Foreach Sink for arbitrary computations on output -- Using the Memory Sink to save output to a table -- Using the File Sink to save output to a partitioned table -- Monitoring streaming queries -- Using Kafka with Spark Structured Streaming -- Introducing Kafka concepts -- Introducing ZooKeeper concepts -- Introducing Kafka-Spark integration -- Introducing Kafka-Spark Structured Streaming -- Writing a receiver for a custom data source -- Summary -- Chapter 6: Using Spark SQL in Machine Learning Applications -- Introducing machine learning applications -- Understanding Spark ML pipelines and their components -- Understanding the steps in a pipeline application development process -- Introducing feature engineering -- Creating new features from raw data…”

Tabla de Contenidos: “…-- 1.2.6 The Fifth Generation-Low-Power and Invisible Computers -- 1.3 THE COMPUTER ZOO -- 1.3.1 Technological and Economic Forces -- 1.3.2 The Computer Spectrum -- 1.3.3 Disposable Computers -- 1.3.4 Microcontrollers -- 1.3.5 Mobile and Game Computers -- 1.3.6 Personal Computers -- 1.3.7 Servers -- 1.3.8 Mainframes -- 1.4 EXAMPLE COMPUTER FAMILIES -- 1.4.1 Introduction to the x86 Architecture -- 1.4.2 Introduction to the ARM Architecture -- 1.4.3 Introduction to the AVR Architecture -- 1.5 METRIC UNITS -- 1.6 OUTLINE OF THIS BOOK -- 2 COMPUTER SYSTEMS -- 2.1 PROCESSORS -- 2.1.1 CPU Organization -- 2.1.2 Instruction Execution -- 2.1.3 RISC versus CISC -- 2.1.4 Design Principles for Modern Computers -- 2.1.5 Instruction-Level Parallelism -- 2.1.6 Processor-Level Parallelism -- 2.2 PRIMARYMEMORY -- 2.2.1 Bits -- 2.2.2 Memory Addresses -- 2.2.3 Byte Ordering -- 2.2.4 Error-Correcting Codes -- 2.2.5 Cache Memory -- 2.2.6 Memory Packaging and Types -- 2.3 SECONDARYMEMORY -- 2.3.1 Memory Hierarchies -- 2.3.2 Magnetic Disks -- 2.3.3 IDE Disks -- 2.3.4 SCSI Disks -- 2.3.5 RAID -- 2.3.6 Solid-State Disks -- 2.3.7 CD-ROMs -- 2.3.8 CD-Recordables -- 2.3.9 CD-Rewritables -- 2.3.10 DVD -- 2.3.11 Blu-ray -- 2.4 INPUT/OUTPUT -- 2.4.1 Buses -- 2.4.2 Terminals -- 2.4.3 Mice -- 2.4.4 Game Controllers…”

Tabla de Contenidos: “…-- 1.1.1 The Operating System as an Extended Machine -- 1.1.2 The Operating System as a Resource Manager -- 1.2 HISTORY OF OPERATING SYSTEMS -- 1.2.1 The First Generation (1945-55): Vacuum Tubes -- 1.2.2 The Second Generation (1955-65): Transistors and Batch Systems -- 1.2.3 The Third Generation (1965-1980): ICs and Multiprogramming -- 1.2.4 The Fourth Generation (1980-Present): Personal Computers -- 1.2.5 The Fifth Generation (1990-Present): Mobile Computers -- 1.3 COMPUTER HARDWARE REVIEW -- 1.3.1 Processors -- 1.3.2 Memory -- 1.3.3 Disks -- 1.3.4 I/O Devices -- 1.3.5 Buses -- 1.3.6 Booting the Computer -- 1.4 THE OPERATING SYSTEM ZOO -- 1.4.1 Mainframe Operating Systems -- 1.4.2 Server Operating Systems -- 1.4.3 Multiprocessor Operating Systems -- 1.4.4 Personal Computer Operating Systems -- 1.4.5 Handheld Computer Operating Systems -- 1.4.6 Embedded Operating Systems -- 1.4.7 Sensor-Node Operating Systems -- 1.4.8 Real-Time Operating Systems -- 1.4.9 Smart Card Operating Systems -- 1.5 OPERATING SYSTEM CONCEPTS -- 1.5.1 Processes -- 1.5.2 Address Spaces -- 1.5.3 Files -- 1.5.4 Input/Output -- 1.5.5 Protection -- 1.5.6 The Shell -- 1.5.7 Ontogeny Recapitulates Phylogeny -- 1.6 SYSTEM CALLS -- 1.6.1 System Calls for Process Management -- 1.6.2 System Calls for File Management -- 1.6.3 System Calls for Directory Management -- 1.6.4 Miscellaneous System Calls -- 1.6.5 The Windows Win32 API -- 1.7 OPERATING SYSTEM STRUCTURE -- 1.7.1 Monolithic Systems -- 1.7.2 Layered Systems -- 1.7.3 Microkernels -- 1.7.4 Client-Server Model -- 1.7.5 Virtual Machines -- 1.8 THE WORLD ACCORDING TO C -- 1.8.1 The C Language -- 1.8.2 Header Files -- 1.8.3 Large Programming Projects -- 1.8.4 The Model of Run Time -- 1.9 RESEARCH ON OPERATING SYSTEMS…”

Tabla de Contenidos: “…Zaha Hadid/Patrick Schumacher -- Estudios para el Zoo Marino, Arq. Barcelona Regional SA, Joan Forgas, Conxita Balcells, Charmaine Lay/Carlos Muro/Roger Paez, Cloud 9 (Enric Ruiz Geli), Taller d'Enginyeria Ambiental/IDVDP arquitectes/Imma Jansana -- Ordenación del complejo deportivo de la Mar Bella, Arq. …”

“…Modern zoos play an important role in animal welfare, conservation, and environmental education; therefore, this shortfall in knowledge may have large, unseen, and negative impacts on these "forgotten species". …”

“…Aunque sus padres eran hermanos, Slim y Tom no se parecen en nada, pero en esta ocasión, de mutuo acuerdo, convienen en desbaratar los turbios manejos de una banda de traficantes de animales salvajes que se dedican a capturar fieras con destino a los zoos extranjeros, exportándolas clandestinamente…”

“…Case studies include the cryopreservation of endangered DNA in frozen zoos, nuclear waste management, seed biobanking, landscape rewilding, social history collecting, space messaging, endangered language documentation, built and natural heritage management, domestic keeping and discarding practices, and world heritage site management"--Page 4 of cover…”

Tabla de Contenidos: “…5.7.7 Smelly GOB -- 5.8 Rule-Based Systems -- 5.8.1 The Problem -- 5.8.2 The Algorithm -- 5.8.3 Pseudo-Code -- 5.8.4 Data Structures and Interfaces -- 5.8.5 Rule Arbitration -- 5.8.6 Unification -- 5.8.7 Rete -- 5.8.8 Extensions -- 5.8.9 Where Next -- 5.9 Blackboard Architectures -- 5.9.1 The Problem -- 5.9.2 The Algorithm -- 5.9.3 Pseudo-Code -- 5.9.4 Data Structures and Interfaces -- 5.9.5 Performance -- 5.9.6 Other Things Are Blackboard Systems -- 5.10 Action Execution -- 5.10.1 Types of Action -- 5.10.2 The Algorithm -- 5.10.3 Pseudo-Code -- 5.10.4 Data Structures and Interfaces -- 5.10.5 Implementation Notes -- 5.10.6 Performance -- 5.10.7 Putting It All Together -- CHAPTER 6: TACTICAL AND STRATEGIC AI -- 6.1 Waypoint Tactics -- 6.1.1 Tactical Locations -- 6.1.2 Using Tactical Locations -- 6.1.3 Generating the Tactical Properties of a Waypoint -- 6.1.4 Automatically Generating the Waypoints -- 6.1.5 The Condensation Algorithm -- 6.2 Tactical Analyses -- 6.2.1 Representing the Game Level -- 6.2.2 Simple Influence Maps -- 6.2.3 Terrain Analysis -- 6.2.4 Learning with Tactical Analyses -- 6.2.5 A Structure for Tactical Analyses -- 6.2.6 Map Flooding -- 6.2.7 Convolution Filters -- 6.2.8 Cellular Automata -- 6.3 Tactical Pathfinding -- 6.3.1 The Cost Function -- 6.3.2 Tactic Weights and Concern Blending -- 6.3.3 Modifying the Pathfinding Heuristic -- 6.3.4 Tactical Graphs for Pathfinding -- 6.3.5 Using Tactical Waypoints -- 6.4 Coordinated Action -- 6.4.1 Multi-Tier AI -- 6.4.2 Emergent Cooperation -- 6.4.3 Scripting Group Actions -- 6.4.4 Military Tactics -- CHAPTER 7: LEARNING -- 7.1 Learning Basics -- 7.1.1 Online or Offline Learning -- 7.1.2 Intra-Behavior Learning -- 7.1.3 Inter-Behavior Learning -- 7.1.4 A Warning -- 7.1.5 Over-Learning -- 7.1.6 The Zoo of Learning Algorithms -- 7.1.7 The Balance of Effort -- 7.2 Parameter Modification…”

Tabla de Contenidos: “…6.7.3 Attention Mechanism Network -- 6.7.4 Graph Convolutional Neural Network -- 6.8 Hands-On of Automobile Fuel Consumption Prediction -- 6.8.1 Dataset -- 6.8.2 Create a Network -- 6.8.3 Training and Testing -- 6.9 References -- Chapter 7: Backward Propagation Algorithm -- 7.1 Derivatives and Gradients -- 7.2 Common Properties of Derivatives -- 7.2.1 Common Derivatives -- 7.2.2 Common Property of Derivatives -- 7.2.3 Hands-On Derivative Finding -- 7.3 Derivative of Activation Function -- 7.3.1 Derivative of Sigmoid Function -- 7.3.2 Derivative of ReLU Function -- 7.3.3 Derivative of LeakyReLU Function -- 7.3.4 Derivative of Tanh Function -- 7.4 Gradient of Loss Function -- 7.4.1 Gradient of Mean Square Error Function -- 7.4.2 Gradient of Cross-Entropy Function -- 7.5 Gradient of Fully Connected Layer -- 7.5.1 Gradient of a Single Neuron -- 7.5.2 Gradient of Fully Connected Layer -- 7.6 Chain Rule -- 7.7 Back Propagation Algorithm -- 7.8 Hands-On Optimization of Himmelblau -- 7.9 Hands-On Back Propagation Algorithm -- 7.9.1 Dataset -- 7.9.2 Network Layer -- 7.9.3 Network model -- 7.9.4 Network Training -- 7.9.5 Network Performance -- 7.10 References -- Chapter 8: Keras Advanced API -- 8.1 Common Functional Modules -- 8.1.1 Common Network Layer Classes -- 8.1.2 Network Container -- 8.2 Model Configuration, Training, and Testing -- 8.2.1 Model Configuration -- 8.2.2 Model Training -- 8.2.3 Model Testing -- 8.3 Model Saving and Loading -- 8.3.1 Tensor Method -- 8.3.2 Network Method -- 8.3.3 SavedModel method -- 8.4 Custom Network -- 8.4.1 Custom Network Layer -- 8.4.2 Customized Network -- 8.5 Model Zoo -- 8.5.1 Load Model -- 8.6 Metrics -- 8.6.1 Create a Metrics Container -- 8.6.2 Write Data -- 8.6.3 Read Statistical Data -- 8.6.4 Clear the Container -- 8.6.5 Hands-On Accuracy Metric -- 8.7 Visualization -- 8.7.1 Model Side -- 8.7.2 Browser Side…”

“…In this vivid, captivating book, Lee Jackson charts the rise of well-known institutions such as gin palaces, music halls, seaside resorts and football clubs, as well as the more peculiar thrills of the pleasure-garden and international expo, from parachuting monkeys to human zoos. He explores how vibrant mass entertainment came to dominate leisure time and how the attempts of religious groups and secular improvers to curb "immorality" in the pub, music hall, and dance hall faltered in the face of commercial success. …”

“…By demonstrating the many ways in which the western models and customs were reshaped, developed, and contested in Central and Eastern European contexts, the authors argue that the dominant way of characterizing these performances as "human zoos" is too narrow. The contributors had to tackle the difficult task of finding traces other than faint copies of official press releases by the tour organizers. …”

“…Get an introduction to container networking by exploring single-host Docker networking modes Examine issues that arise when you move to multihost container networking Maintain a map of running containers and their locations with service discovery tools such as ZooKeeper and etcd Configure network interfaces in Linux containers by using plugins with the Container Network Interface (CNI) Learn how the Kubernetes orchestration system approaches container networking…”

“…Weaving together a vast corpus of colonial French children's comics, Francophone novels, and African popular music, fashion, and dance, Knox traces how the ways colonial 'human zoos' invited their French spectators to gaze on their colonized others still inform the frameworks through which racial and ethnic minorities are made-and make themselves-visible in contemporary France. …”

“…GPU, TPU) and execution backends (e.g. web browser, unikernel) of numerical computation Use the Zoo system for efficient scripting, code sharing, service deployment, and composition Design and implement a distributed computing engine to work with a numerical computing library, providing convenient APIs and high performance…”

“…While this includes locations out in the wild, she also covers zoos, parks, sanctuaries, and other easily accessible spots—which are especially great for beginning bird photographers. …”