Tabla de Contenidos: “…Preface; 1. Quarks and gluons; 2. Lattices; 3. Path integrals and statistical mechanics; 4. …”

“…The spectacular successes of this technique include compelling evidence that exchange of gauge gluons can confine the quarks within subnuclear matter. …”

Tabla de Contenidos: “…Lattice quantum chromodynamics; 16. Perturbative quark-gluon plasma; Part VI. Strangeness: 17. Thermal flavor production in deconfined phase; 18. …”

Materias: “…Quark-gluon plasma…”

Tabla de Contenidos: “…Gluon emission via the Bremsstrahlung process; 16. …”

Materias:

Materias:

Materias:

Materias:

Materias:

Materias:

Tabla de Contenidos: “…A simple example; 3. The reggeized gluon; 4. The QCD Pomeron; 5. From cuts to poles; 6. …”

Tabla de Contenidos: “…Dipole approach to high parton density QCD; 5. Classical gluon fields and the Color Glass Condensate; 6. Corrections to non-linear evolution equations; 7. …”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright and Credits -- Foreword -- Contributors -- Table of Contents -- Preface -- Chapter 1: Up and Running with MXNet -- Technical requirements -- Installing MXNet, Gluon, GluonCV, and GluonNLP -- Getting ready -- How to do it... -- How it works... -- There's more... -- NumPy and MXNet ND arrays -- Getting ready -- How to do it... -- How it works... -- There's more... -- Chapter 2: Working with MXNet and Visualizing Datasets - Gluon and DataLoader -- Technical requirements -- Understanding regression datasets - loading, managing, and visualizing the House Sales dataset -- Getting ready -- How to do it... -- How it works... -- There's more... -- Understanding classification datasets - loading, managing, and visualizing the Iris dataset -- Getting ready -- How to do it... -- How it works... -- There's more... -- Understanding image datasets - loading, managing, and visualizing the Fashion-MNIST dataset -- Getting ready -- How to do it... -- How it works... -- There's more... -- Understanding text datasets - loading, managing, and visualizing the Enron Email dataset -- Getting ready -- How to do it... -- How it works... -- There's more... -- Chapter 3: Solving Regression Problems -- Technical requirements -- Understanding the math of regression models -- Getting ready -- How to do it... -- How it works... -- There's more... -- Defining loss functions and evaluation metrics for regression -- Getting ready -- How to do it... -- How it works... -- There's more... -- Training regression models -- Getting ready -- How to do it... -- How it works... -- There's more... -- Evaluating regression models -- Getting ready -- How to do it... -- How it works... -- There's more... -- Chapter 4: Solving Classification Problems -- Technical requirements -- Understanding math for classification models -- Getting ready -- How to do it…”

Tabla de Contenidos: “…Schwinger-Dyson equations in the pinch technique framework; 7. Non-perturbative gluon mass and quantum solitons; 8. Nexuses, sphalerons, and fractional topological charge; 9. …”

“…Heavy ion collision experiments recreating the quark-gluon plasma that filled the microseconds-old universe have established that it is a nearly perfect liquid that flows with such minimal dissipation that it cannot be seen as made of particles. …”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright and Credits -- Foreword -- Contributors -- Table of Contents -- Preface -- Section 1: Fundamentals of the Automated Machine Learning Process and AutoML on AWS -- Chapter 1: Getting Started with Automated Machine Learning on AWS -- Technical requirements -- Overview of the ML process -- Complexities in the ML process -- An example of the end-to-end ML process -- Introducing ACME Fishing Logistics -- The case for ML -- Getting insights from the data -- Building the right model -- Training the model -- Evaluating the trained model -- Exploring possible next steps -- Tuning our model -- Deploying the optimized model into production -- Streamlining the ML process with AutoML -- How AWS makes automating the ML development and deployment process easier -- Summary -- Chapter 2: Automating Machine Learning Model Development Using SageMaker Autopilot -- Technical requirements -- Introducing the AWS AI and ML landscape -- Overview of SageMaker Autopilot -- Overcoming automation challenges with SageMaker Autopilot -- Getting started with SageMaker Studio -- Preparing the experiment data -- Starting the Autopilot experiment -- Running the Autopilot experiment -- Post-experimentation tasks -- Using the SageMaker SDK to automate the ML experiment -- Codifying the Autopilot experiment -- Analyzing the Autopilot experiment with code -- Deploying the best candidate -- Cleaning up -- Summary -- Chapter 3: Automating Complicated Model Development with AutoGluon -- Technical requirements -- Introducing the AutoGluon library -- Using AutoGluon for tabular data -- Prerequisites -- Creating the AutoML experiment with AutoGluon -- Evaluating the experiment results -- Using AutoGluon for image data -- Prerequisites -- Creating an image prediction experiment -- Evaluating the experiment results -- Summary…”