Tabla de Contenidos: “…Geospatial Python Toolbox -- Installing third-party Python modules -- Installing GDAL -- Windows -- Linux -- Mac OS X -- Python networking libraries for acquiring data -- Python urllib module -- FTP -- ZIP and TAR files -- Python markup and tag-based parsers -- The minidom module -- ElementTree -- Building XML -- WKT -- Python JSON libraries -- json module -- geojson module -- OGR -- PyShp -- dbfpy -- Shapely -- GDAL -- NumPy -- PIL -- PNGCanvas -- PyFPDF -- Spectral Python -- Summary -- 5. …”

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…”

“…You will learn the basics of Python, with a focus on data science, as well as the essential tools for cleaning and examining data, plotting with Matplotlib, and working with NumPy and Pandas. With this foundation in place, you will dive deep into the world of deep learning, starting with the MP Neuron model and progressing to the Perceptron, the Sigmoid Neuron, and the Universal Approximation Theorem. …”

“…This course melds theory and practice, introducing everything from the foundational aspects of image processing to the hands-on implementation of advanced deep learning models in PyTorch, including applications on edge devices. …”

“…You will make use of NumPy, pandas, and Matplotlib for visualizations and also go through forecasting techniques used in technical analysis. …”

“…We then move on to the Python NumPy library, which supports large arrays and matrices. …”

“…You will learn to use the power of Python to train your machine and make predictions and implement the ML algorithm "Random Forest." Use NumPy with Python for array handling, Pandas data frames for Excel files, and matplotlib for data visualization. …”

“…Following the clear examples and precisely articulated details, you'll learn how to use common libraries like NumPy and pandas in more performant ways and transform data for efficient storage and I/O. …”

“…In Designing Deep Learning Systems you will learn how to: Transfer your software development skills to deep learning systems Recognize and solve common engineering challenges for deep learning systems Understand the deep learning development cycle Automate training for models in TensorFlow and PyTorch Optimize dataset management, training, model serving and hyperparameter tuning Pick the right open-source project for your platform Deep learning systems are the components and infrastructure essential to supporting a deep learning model in a production environment. …”

“…Following the clear examples and precisely articulated details, you'll learn how to use common libraries like NumPy and pandas in more performant ways and transform data for efficient storage and I/O. …”

Texto completo en Odilo

Tabla de Contenidos: “…-- Understanding geospatial databases -- Sharing data with interchange formats -- Introducing spatiotemporal data -- Summary -- Questions -- Further reading -- Chapter 3: The Geospatial Technology Landscape -- Technical requirements -- Understanding data access -- GDAL -- PDAL -- Understanding computational geometry -- The PROJ projection library -- CGAL -- JTS -- GEOS -- PostGIS -- Other spatially enabled databases -- Routing -- Understanding desktop tools (including visualization) -- Quantum GIS -- GRASS GIS -- gvSIG -- OpenJUMP -- Google Earth -- NASA WorldWind -- ArcGIS -- Leaflet and OpenLayers -- Understanding metadata management -- Python's pycsw library -- GeoNode -- GeoNetwork -- A quick look at artificial intelligence -- Summary -- Questions -- Further reading -- Part 2: Geospatial Analysis Concepts -- Chapter 4: Geospatial Python Toolbox -- Technical requirements -- Using QGIS -- Installing third-party Python modules -- Anaconda -- Jupyter -- PyPI and pip -- The Python virtualenv module -- Python networking libraries for acquiring data -- The Python urllib module -- The Python requests module -- FTP -- Bundling and compressing files -- Python markup and tag-based parsers -- The minidom module -- The ElementTree module -- Building XML using ElementTree and minidom -- Well-Known Text (WKT) -- Python JSON libraries -- The json module -- The geojson module -- OGR -- PyShp -- Shapely -- Fiona -- GDAL -- NumPy -- PIL -- PNGCanvas -- GeoPandas -- PyFPDF -- PyMySQL -- Rasterio -- OSMnx -- Folium -- Summary -- Questions -- Further reading -- Chapter 5: Python and Geospatial Algorithms -- Technical requirements -- Measuring distance -- Using the Pythagorean theorem to measure distance -- Using the haversine formula -- Using the Vincenty formula -- Calculating line direction…”

Tabla de Contenidos: “…Privacy-enhancing technologies -- Differential privacy -- Federated learning -- Secure multi-party computation (SMC) -- Homomorphic encryption -- Anonymization -- De-identification -- Differential privacy -- Summary -- Chapter 4: Overview of Differential Privacy Algorithms and Applications of Differential Privacy -- Differential privacy algorithms -- Laplace distribution -- Gaussian distribution -- Comparison of noise-adding algorithms to apply differential privacy -- Generating aggregates using differential privacy -- Sensitivity -- Queries that use differential privacy -- Clipping -- Overview of real-life applications of differential privacy -- Differential privacy usage at Uber -- Differential privacy usage at Apple -- Differential privacy usage in the US Census -- Differential privacy at Google -- Summary -- Chapter 5: Developing Applications with Differential Privacy Using Open Source Frameworks -- Open source frameworks to implement differential privacy -- Introduction to the PyDP framework and its key features -- Examples and demonstrations of PyDP in action -- Developing a sample banking application with PyDP to showcase differential privacy techniques -- Protecting against membership inference attacks -- Applying differential privacy to large datasets -- Use case - generating differentially private aggregates on a large dataset -- PipelineDP high-level architecture -- Tumult Analytics -- Machine learning using differential privacy -- Synthetic Dataset Generation: Introducing Fraudulent Transactions -- Develop a classification model using scikit-learn -- High-level implementation of the SGD algorithm -- Applying differential privacy options using machine learning -- Generating gradients using differential privacy -- Clustering using differential privacy -- Deep learning using differential privacy -- Fraud detection model using PyTorch…”

Tabla de Contenidos: “…The traditional change management process -- Introduction to continuous integration -- Installing GitLab -- GitLab runners -- First GitLab example -- GitLab network example -- Summary -- Chapter 16: Test-Driven Development for Networks -- Test-driven development overview -- Test definitions -- Topology as code -- XML parsing example -- Python's unittest module -- More on Python testing -- pytest examples -- Writing tests for networking -- Testing for reachability -- Testing for network latency -- Testing for security -- Testing for transactions -- Testing for network configuration -- Testing for Ansible -- pyATS and Genie -- Summary -- Other Books You May Enjoy -- Index…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright -- Dedication -- Contributors -- Table of Contents -- Preface -- Chapter 1: An Introduction to Basic Packages, Functions, and Concepts -- Technical requirements -- Exploring Python numerical types -- Decimal type -- Fraction type -- Complex type -- Understanding basic mathematical functions -- Diving into the world of NumPy -- Element access -- Array arithmetic and functions -- Useful array creation routines -- Higher-dimensional arrays -- Working with matrices and linear algebra -- Basic methods and properties -- Matrix multiplication -- Determinants and inverses -- Systems of equations -- Eigenvalues and eigenvectors -- Sparse matrices -- Summary -- Further reading -- Chapter 2: Mathematical Plotting with Matplotlib -- Technical requirements -- Basic plotting with Matplotlib -- Getting ready -- How to do it... -- How it works... -- There's more... -- Adding subplots -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Plotting with error bars -- Getting ready -- How to do it... -- How it works... -- There's more... -- Saving Matplotlib figures -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Surface and contour plots -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Customizing three-dimensional plots -- Getting ready -- How to do it... -- How it works... -- There's more... -- Plotting vector fields with quiver plots -- Getting ready -- How to do it... -- How it works... -- There's more... -- Further reading -- Chapter 3: Calculus and Differential Equations -- Technical requirements -- Primer on calculus -- Working with polynomials and calculus -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Differentiating and integrating symbolically using SymPy -- Getting ready…”