Texto completo en Odilo

Texto completo en Odilo

Texto completo en Odilo

“…Get a better grasp of NumPy, Cython, and profilers Learn how Python abstracts the underlying computer architecture Use profiling to find bottlenecks in CPU time and memory usage Write efficient programs by choosing appropriate data structures Speed up matrix and vector computations Use tools to compile Python down to machine code Manage multiple I/O and computational operations concurrently Convert multiprocessing code to run on local or remote clusters Deploy code faster using tools like Docker…”

“…You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. …”

“…Gradually, you'll move on to review statistical inference using Python, Pandas, and SciPy. After that, we'll focus on performing regression using computational tools and you'll get to understand the problem of identifying clusters in data in an algorithmic way. …”

“…There will be an introductory chapter covering basic concepts and terminology, and one chapter each on NumPy(the scientific computation library), Pandas (the data wrangling library) and visualization libraries like Matplotlib and Seaborn. …”

“…Set up a proper Python environment for algorithmic trading Learn how to retrieve financial data from public and proprietary data sources Explore vectorization for financial analytics with NumPy and pandas Master vectorized backtesting of different algorithmic trading strategies Generate market predictions by using machine learning and deep learning Tackle real-time processing of streaming data with socket programming tools Implement automated algorithmic trading strategies with the OANDA and FXCM platforms…”

“…You will learn how to build logistic regression models in scikit-learn and PySpark, and you will go through the process of hyperparameter tuning with a validation data set. …”

Tabla de Contenidos: “…Costos para las PyME; Página legal; Tabla de contenido; Agradecimientos; Introducción; Módulo 1; La importancia de lainformación financiera; Ejemplo. …”

Tabla de Contenidos: “…Identifying potential microservices -- Code complexity and maintenance -- Metrics and Monitoring -- Logging -- Splitting a Monolith -- Feature Flags -- Refactoring Jeeves -- Workflow -- Summary -- Chapter 6: Interacting with Other Services -- Calling other web resources -- Finding out where to go -- Environment variables -- Service discovery -- Transferring data -- HTTP cache headers -- GZIP compression -- Protocol Buffers -- MessagePack -- Putting it together -- Asynchronous messages -- Message queue reliability -- Basic queues -- Topic exchanges and queues -- Publish/subscribe -- Putting it together -- Testing -- Using OpenAPI -- Summary -- Chapter 7: Securing Your Services -- The OAuth2 protocol -- X.509 certificate-based authentication -- Token-based authentication -- The JWT standard -- PyJWT -- Using a certificate with JWT -- The TokenDealer microservice -- The OAuth implementation -- Using TokenDealer -- Securing your code -- Limiting your application scope -- Untrusted incoming data -- Redirecting and trusting queries -- Sanitizing input data -- Using Bandit linter -- Dependencies -- Web application firewall -- OpenResty: Lua and nginx -- Rate and concurrency limiting -- Other OpenResty features -- Summary -- Chapter 8: Making a Dashboard -- Building a ReactJS dashboard -- The JSX syntax -- React components -- Pre-processing JSX -- ReactJS and Quart -- Cross-origin resource sharing -- Authentication and authorization -- A note about Micro Frontends -- Getting the Slack token -- JavaScript authentication -- Summary -- Chapter 9: Packaging and Running Python -- The packaging toolchain -- A few definitions -- Packaging -- The setup.py file -- The requirements.txt file -- The MANIFEST.in file -- Versioning -- Releasing -- Distributing -- Running all microservices -- Process management -- Summary -- Chapter 10: Deploying on AWS -- What is Docker?…”

Tabla de Contenidos: “…Introduction to Python -- B.1 Variables -- B.1.1 Control flow -- B.1.2 Collections -- B.1.3 Code reusability -- B.1.4 Installing libraries -- B.1.5 Python programs -- Appendix C. Introduction to NumPy -- C.1 NumPy -- C.1.1 NumPy arrays -- C.1.2 Two-dimensional NumPy arrays -- C.1.3 Randomly generated arrays -- C.2 NumPy operations -- C.2.1 Element-wise operations -- C.2.2 Summarizing operations -- C.2.3 Sorting -- C.2.4 Reshaping and combining -- C.2.5 Slicing and filtering…”

Tabla de Contenidos: “…-- Grasping Python's Core Philosophy -- Contributing to data science -- Discovering present and future development goals -- Working with Python -- Getting a taste of the language -- Understanding the need for indentation -- Working at the command line or in the IDE -- Performing Rapid Prototyping and Experimentation -- Considering Speed of Execution -- Visualizing Power -- Using the Python Ecosystem for Data Science -- Accessing scientific tools using SciPy -- Performing fundamental scientific computing using NumPy -- Performing data analysis using pandas -- Implementing machine learning using Scikit-learn -- Going for deep learning with Keras and TensorFlow -- Plotting the data using matplotlib -- Creating graphs with NetworkX -- Parsing HTML documents using Beautiful Soup -- Chapter 3 Setting Up Python for Data Science…”

Texto completo en Odilo

Texto completo en Odilo

Texto completo en Odilo

Texto completo en Odilo

Texto completo en Odilo

Texto completo en Odilo

Texto completo en Odilo