“…Generic model structures and dependable libraries of sub-models that can be applied repeatedly are increasingly important. Applications ar…”

“…Instead, you get highly focused instruction in the core elements of Java 1.4, accompanied by carefully chosen examples and line-by-line analyses that ar…”

“…Chapters in the book address the following ar…”

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Tabla de Contenidos: “…Forecasting with statistical models -- 4 Modeling a moving average process -- 4.1 Defining a moving average process -- 4.1.1 Identifying the order of a moving average process -- 4.2 Forecasting a moving average process -- 4.3 Next steps -- 4.4 Exercises -- 4.4.1 Simulate an MA(2) process and make forecasts -- 4.4.2 Simulate an MA(q) process and make forecasts -- Summary -- 5 Modeling an autoregressive process -- 5.1 Predicting the average weekly foot traffic in a retail store -- 5.2 Defining the autoregressive process -- 5.3 Finding the order of a stationary autoregressive process -- 5.3.1 The partial autocorrelation function (PACF) -- 5.4 Forecasting an autoregressive process -- 5.5 Next steps -- 5.6 Exercises -- 5.6.1 Simulate an AR(2) process and make forecasts -- 5.6.2 Simulate an AR(p) process and make forecasts -- Summary -- 6 Modeling complex time series -- 6.1 Forecasting bandwidth usage for data centers -- 6.2 Examining the autoregressive moving average process -- 6.3 Identifying a stationary ARMA process -- 6.4 Devising a general modeling procedure -- 6.4.1 Understanding the Akaike information criterion (AIC) -- 6.4.2 Selecting a model using the AIC -- 6.4.3 Understanding residual analysis -- 6.4.4 Performing residual analysis -- 6.5 Applying the general modeling procedure -- 6.6 Forecasting bandwidth usage -- 6.7 Next steps -- 6.8 Exercises -- 6.8.1 Make predictions on the simulated ARMA(1,1) process -- 6.8.2 Simulate an ARMA(2,2) process and make forecasts -- Summary -- 7 Forecasting non-stationary time series -- 7.1 Defining the autoregressive integrated moving average model -- 7.2 Modifying the general modeling procedure to account for non-stationary series…”

Tabla de Contenidos: “…Testuak analiza tzeko programa informatiko ba tzuk; Bibliografia; 3.3. Eztabaida taldeak eta ar da tz taldeak; 3.3.1. Sarrera; 3.3.2. Eztabaida taldeak sor tzeko irizpideak. …”

Tabla de Contenidos: “…3.1.2 Funciones polinomiales3.1.3 Funciones racionales; 3.1.4 Funciones algebraicas; 3.1.5 Potencias racionales ar; 3.1.6 Leyes de los exponentes racionales; 3.1.7 La función exponencial ax y la función logaritmo logex; 3.1.8 Funciones trigonométricas; 3.1.8.1 Círculo trigonométrico. …”

Tabla de Contenidos: “…Instrumentos impresos de búsqueda; 1.1 Elenchus of Biblica (EBB); 1.2 Zeitschrifteninhaltsdienst Theologie (ZID); 1.3 Neuerwerbungen Theologie und allgemeine Religionswissenschaft (NThAR)…”

Tabla de Contenidos: “…1.5.5 Portability of Functionality1.5.6 Performance Portability; 1.5.7 Safety, Determinism, and Maintainability; 1.5.8 Overview of Programming Models Used; Cilk Plus; Threading Building Blocks (TBB); OpenMP; Array Building Blocks (ArBB); OpenCL; 1.5.9 When to Use Which Model?; 1.6 Organization of this Book; 1.7 Summary; 2 Background; 2.1 Vocabulary and Notation; 2.2 Strategies; 2.3 Mechanisms; 2.4 Machine Models; 2.4.1 Machine Model; Instruction Parallelism; Memory Hierarchy; Virtual Memory; Multiprocessor Systems; Attached Devices; 2.4.2 Key Features for Performance; Data Locality…”

Tabla de Contenidos: “…Evolving language models - the AR Transformer and its role in GenAI -- Implementing the original Transformer -- Data loading and preparation -- Tokenization -- Data tensorization -- Dataset creation -- Embeddings layer -- Positional encoding -- Multi-head self-attention -- FFN -- Encoder layer -- Encoder -- Decoder layer -- Decoder -- Complete transformer -- Training function -- Translation function -- Main execution -- Summary -- References -- Chapter 4: Applying Pretrained Generative Models: From Prototype to Production -- Prototyping environments -- Transitioning to production -- Mapping features to production setup -- Setting up a production-ready environment -- Local development setup -- Visual Studio Code -- Project initialization -- Docker setup -- Requirements file -- Application code -- Creating a code repository -- CI/CD setup -- Model selection - choosing the right pretrained generative model -- Meeting project objectives -- Model size and computational complexity -- Benchmarking -- Updating the prototyping environment -- GPU configuration -- Loading pretrained models with LangChain -- Setting up testing data -- Quantitative metrics evaluation -- Alignment with CLIP -- Interpreting outcomes -- Responsible AI considerations -- Addressing and mitigating biases -- Transparency and explainability -- Final deployment -- Testing and monitoring -- Maintenance and reliability -- Summary -- Part 2: Practical Applications of Generative AI -- Chapter 5: Fine-Tuning Generative Models for Specific Tasks -- Foundation and relevance - an introduction to fine-tuning -- PEFT -- LoRA -- AdaLoRA -- In-context learning -- Fine-tuning versus in-context learning -- Practice project: Fine-tuning for Q&amp -- A using PEFT -- Background regarding question-answering fine-tuning -- Implementation in Python -- Evaluation of results -- Summary -- References…”

Tabla de Contenidos: “…Creating a Self-Driving Car Using ROS -- Getting started with self-driving cars -- History of autonomous vehicles -- Levels of autonomy -- Functional block diagram of a typical self-driving car -- GPS, IMU, and wheel encoders -- Xsens MTi IMU -- Camera -- Ultrasonic sensors -- LIDAR and RADAR -- Velodyne HDL-64 LIDAR -- SICK LMS 5xx/1xx and Hokuyo LIDAR -- Continental ARS 300 radar (ARS) -- Delphi radar -- On-board computer -- Software block diagram of self-driving cars -- Simulating the Velodyne LIDAR -- Interfacing Velodyne sensors with ROS -- Simulating a laser scanner -- Explaining the simulation code -- Interfacing laser scanners with ROS -- Simulating stereo and mono cameras in Gazebo -- Interfacing cameras with ROS -- Simulating GPS in Gazebo -- Interfacing GPS with ROS -- Simulating IMU on Gazebo -- Interfacing IMUs with ROS -- Simulating an ultrasonic sensor in Gazebo -- Low-cost LIDAR sensors -- Sweep LIDAR -- RPLIDAR -- Simulating a self-driving car with sensors in Gazebo -- Installing prerequisites -- Visualizing robotic car sensor data -- Moving a self-driving car in Gazebo -- Running hector SLAM using a robotic car -- Interfacing a DBW car with ROS -- Installing packages -- Visualizing the self-driving car and sensor data -- Communicating with DBW from ROS -- Introducing the Udacity open source self-driving car project -- MATLAB ADAS toolbox -- Questions -- Summary -- 11. …”

Tabla de Contenidos: “…Uhl -- Evaluation of the Menzies method potential for automatic dermoscopic image analysis / A.R.S. Marcal. [et al.] -- Application of UTHSCSA-Image Tool to estimate concrete texture / J.S. …”

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