“…Several resources exist for individual pieces of this data science stack, but only with the new edition of Python Data Science Handbook do you get them all--IPython, NumPy, pandas, Matplotlib, Scikit-Learn, and other related tools. …”

“…Using only Python and its math-supporting library, NumPy, you’ll train your own neural networks to see and understand images, translate text into different languages, and even write like Shakespeare! …”

Tabla de Contenidos: “…-- PhoneGap's Architecture -- Anaconda Python and the PyQT5 GUI Framework -- Anaconda Installation -- Linux -- Windows -- PyQT5 Installation -- Linux -- Windows -- PyQt5 Example -- PyQT Events -- Drawbacks to Gamification -- Avoiding the Drawbacks -- Summary -- Chapter 8: Human-Computer Interaction Research and Development -- Human-Computer Interaction with a Head-Mounted Display -- Human-Machine Interfaces: Future Development -- The Touchscreen Revolution -- Direct Communication with the Mind -- Gesture Engagement Taken to a New Level -- Applications of Spatial Cognition Human Contact Research -- Interaction with the Voice -- Interactions Between the Brain and the Computer -- Summary -- Chapter 9: Recommendations and Concluding Comments -- Recommendations -- Broad HCI Assessment Criteria -- Information and Communication Technology (ICT) Development -- New Trends…”

Texto completo en Odilo

Texto completo en Odilo

Texto completo en Odilo

Tabla de Contenidos: “…Hands-on lab - running distributed model training with PyTorch…”

Tabla de Contenidos: “…Cisco NX-API -- Lab software installation and device preparation -- NX-API examples -- The Cisco and YANG models -- The Cisco ACI -- The Python API for Juniper networks -- Juniper and NETCONF -- Device preparation -- Juniper NETCONF examples -- Juniper PyEZ for developers -- Installation and preparation -- PyEZ examples -- The Arista Python API -- Arista eAPI management -- The eAPI preparation -- eAPI examples -- The Arista Pyeapi library -- Pyeapi installation -- Pyeapi examples -- Vendor-neutral libraries -- Summary -- Chapter 4: The Python Automation Framework - Ansible Basics -- A more declarative framework -- A quick Ansible example -- The control node installation -- Running different versions of Ansible from source -- Lab setup -- Your first Ansible playbook -- The public key authorization -- The inventory file -- Our first playbook -- The advantages of Ansible -- Agentless -- Idempotent -- Simple and extensible -- Network vendor support -- The Ansible architecture -- YAML -- Inventories -- Variables -- Templates with Jinja2 -- Ansible networking modules -- Local connections and facts -- Provider arguments -- The Ansible Cisco example -- Ansible 2.5 connection example -- The Ansible Juniper example -- The Ansible Arista example -- Summary -- Chapter 5: The Python Automation Framework - Beyond Basics -- Ansible conditionals -- The when clause -- Ansible network facts -- Network module conditional -- Ansible loops -- Standard loops -- Looping over dictionaries -- Templates -- The Jinja2 template -- Jinja2 loops -- The Jinja2 conditional -- Group and host variables -- Group variables -- Host variables -- The Ansible Vault -- The Ansible include and roles -- The Ansible include statement -- Ansible roles -- Writing your own custom module -- The first custom module -- The second custom module -- Summary -- Chapter 6: Network Security with Python…”

“…A basic knowledge of Python/NumPy is recommended. Some skills in mathematics will help you understand the theory behind the computational methods. …”

“…A comprehensive guide to help you understand the principles of Reactive and asynchronous programming and its benefits Key Features Explore the advantages of Reactive programming Use concurrency and parallelism in RxPY to build powerful reactive applications Deploy and scale your reactive applications using Docker Book Description Reactive programming is central to many concurrent systems, but it's famous for its steep learning curve, which makes most developers feel like they're hitting a wall. …”

“…Understand text mining, machine learning, and network analysis; process numeric data with the NumPy and Pandas modules; describe and analyze data using statistical and network-theoretical methods; and see actual examples of data analysis at work. …”

“…As you progress, gain proficiency in NumPy for numerical operations and Pandas for data manipulation, mastering the skills to handle and transform data efficiently. …”

“…This book includes the RecSys platform components, relevant MLOps tools in your stack, plus code examples and helpful suggestions in PySpark, SparkSQL, FastAPI, and Weights & Biases…”

“…The Python modules span from basic syntax to advanced data manipulation with libraries like NumPy and pandas, providing a solid foundation for data analysis and model building. …”

“…The course begins with a complete introduction to the capabilities and features of Python and how to set up the language on your computer along with PyCharm IDE. You will learn about multiple programming-paradigms (object-oriented, functional, and imperative). …”

Tabla de Contenidos: “…Cover -- Copyright -- Credits -- About the Author -- About the Reviewers -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Python Basics -- Python installation -- Installation of Python via Anaconda -- Launching Python via Spyder -- Direct installation of Python -- Variable assignment, empty space, and writing our own programs -- Writing a Python function -- Python loops -- Python loops, if...else conditions -- Data input -- Data manipulation -- Data output -- Exercises -- Summary -- Chapter 2: Introduction to Python Modules -- What is a Python module? -- Introduction to NumPy -- Introduction to SciPy -- Introduction to matplotlib -- How to install matplotlib -- Several graphical presentations using matplotlib -- Introduction to statsmodels -- Introduction to pandas -- Python modules related to finance -- Introduction to the pandas_reader module -- Two financial calculators -- How to install a Python module -- Module dependency -- Exercises -- Summary -- Chapter 3: Time Value of Money -- Introduction to time value of money -- Writing a financial calculator in Python -- Definition of NPV and NPV rule -- Definition of IRR and IRR rule -- Definition of payback period and payback period rule -- Writing your own financial calculator in Python -- Two general formulae for many functions -- Appendix A - Installation of Python, NumPy, and SciPy -- Appendix B - visual presentation of time value of money -- Appendix C - Derivation of present value of annuity from present value of one future cash flow and present value of perpetuity -- Appendix D - How to download a free financial calculator written in Python -- Appendix E - The graphical presentation of the relationship between NPV and R -- Appendix F - graphical presentation of NPV profile with two IRRs -- Appendix G - Writing your own financial calculator in Python…”

Tabla de Contenidos: “…AGUAENERGÍA; CONCLUSIONES; BIBLIOGRAFÍA; 4 PROMOCIÓN Y APOYO EMPRESARIAL PROGRAMAS Y SERVICIOS; INTRODUCCIÓN; PROGRAMAS DE APOYO A LAS PyMES EN MÉXICO; PROGRAMA NACIONAL DE APOYO PARA LAS EMPRESAS DE SOLIDARIDAD (FONAES); FOFOMINE (FONDO PARA EL FOMENTO DE LA MICROEMPRESA EN NEZAHUALCÓYOTL); PROGRAMAS DE APOYO PARA EL DESARROLLO TECNOLÓGICO E INNOVACIÓN; BANCOMEXT; BANCOMEXT Y PROMEXICO; CONCLUSIONES; BIBLIOGRAFÍA…”

Tabla de Contenidos:

Tabla de Contenidos: “…; Persistent cross-site scripting; Nonpersistent cross-site scripting; Examples of cross-site scripting; A simple to-do app using Tornado/Python; Coding up server.py; Cross-site scripting example 1; Cross-site scripting example 2; Cross-site scripting example 3; Defending against cross-site scripting; Do not trust users - parsing input by users…”

Tabla de Contenidos: “…Mixture models and clustering -- Non-finite mixture model -- Dirichlet process -- Continuous mixtures -- Some common distributions are mixtures -- Summary -- Exercises -- Chapter 8: Gaussian Processes -- Linear models and non-linear data -- Modeling functions -- Multivariate Gaussians and functions -- Covariance functions and kernels -- Gaussian processes -- Gaussian process regression -- Gaussian process regression with PyMC -- Setting priors for the length scale -- Gaussian process classification -- GPs for space flu -- Cox processes -- Coal mining disasters -- Red wood -- Regression with spatial autocorrelation -- Hilbert space GPs -- HSGP with Bambi -- Summary -- Exercises -- Chapter 9: Bayesian Additive Regression Trees -- Decision trees -- BART models -- Bartian penguins -- Partial dependence plots -- Individual conditional plots -- Variable selection with BART -- Distributional BART models -- Constant and linear response -- Choosing the number of trees -- Summary -- Exercises -- Chapter 10: Inference Engines -- Inference engines -- The grid method -- Quadratic method -- Markovian methods -- Monte Carlo -- Markov chain -- Metropolis-Hastings -- Hamiltonian Monte Carlo -- Sequential Monte Carlo -- Diagnosing the samples -- Convergence -- Trace plot -- Rank plot -- , (R hat) -- Effective Sample Size (ESS) -- Monte Carlo standard error -- Divergences -- Keep calm and keep trying -- Summary -- Exercises -- Chapter 11: Where to Go Next -- Other Books You May Enjoy -- Index…”