Tabla de Contenidos: “…B -- C -- Appendix C Code for search.py -- A -- B -- C -- D -- Appendix D Pseudocode for faceit.py -- Appendix E The Data Visualisation Catalogue's Visualization Types -- Appendix F Glossary -- Index -- EULA…”

Tabla de Contenidos:

Tabla de Contenidos: “…""Installing the PHP Driver on Unix-Based Platforms Automatically""""Installing the PHP Driver on Unix-Based Platforms Manually""; ""Installing the PHP Driver on Windows""; ""Confirming That Your PHP Installation Works""; ""Connecting to and Disconnecting from the PHP Driver""; ""Installing the Python Driver""; ""Installing PyMongo under Linux""; ""Installing PyMongo Automatically""; ""Installing PyMongo Manually""; ""Installing PyMongo under Windows""; ""Confirming That Your PyMongo Installation Works""; ""Summary""; ""Chapter 3: The Data Model""; ""Designing the Database""…”

Tabla de Contenidos: “…Rapid Introduction to Procedural Programming -- Creating and Running Python Programs -- Python's "Beautiful Heart" -- Piece #1: Data Types -- Piece #2: Object References -- Piece #3: Collection Data Types -- Piece #4: Logical Operations -- Piece #5: Control Flow Statements -- Piece #6: Arithmetic Operators -- Piece #7: Input/Output -- Piece #8: Creating and Calling Functions -- Examples -- bigdigits.py -- generate_grid.py -- Summary -- Exercises -- Chapter 2. …”

Tabla de Contenidos: “…-- Accessing the WWW with Python -- Setting things up -- Creating a virtual environment -- Installing libraries -- Loading URLs -- URL handling and operations -- requests - Python library -- Implementing HTTP methods -- GET -- POST -- Summary -- Further reading -- Part 2: Beginning Web Scraping -- Chapter 3: Searching and Processing Web Documents -- Technical requirements -- Introducing XPath and CSS selectors to process markup documents -- The Document Object Model (DOM) -- XPath -- CSS selectors -- Using web browser DevTools to access web content -- HTML elements and DOM navigation -- XPath and CSS selectors using DevTools -- Scraping using lxml - a Python library -- lxml by example -- Web scraping using lxml -- Parsing robots.txt and sitemap.xml -- The robots.txt file -- Sitemaps -- Summary -- Further reading -- Chapter 4: Scraping Using PyQuery, a jQuery-Like Library for Python -- Technical requirements -- PyQuery overview -- Introducing jQuery -- Exploring PyQuery -- Installing PyQuery -- Loading a web URL -- Element traversing, attributes, and pseudo-classes -- Iterating using PyQuery -- Web scraping using PyQuery -- Example 1 - scraping book details -- Example 2 - sitemap to CSV -- Example 3 - scraping quotes with author details -- Summary -- Further reading…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright and Credits -- About Packt -- Contributors -- Table of Contents -- Preface -- Chapter 1: Pyspark and Setting up Your Development Environment -- An overview of PySpark -- Spark SQL -- Setting up Spark on Windows and PySpark -- Core concepts in Spark and PySpark -- SparkContext -- Spark shell -- SparkConf -- Summary -- Chapter 2: Getting Your Big Data into the Spark Environment Using RDDs -- Loading data on to Spark RDDs -- The UCI machine learning repository -- Getting the data from the repository to Spark -- Getting data into Spark -- Parallelization with Spark RDDs -- What is parallelization? …”

Tabla de Contenidos: “…Reloading Servlet ModulesJython and XML; SAX and DOM; JDOM and Jython; Jython Standard Library; Using Python Modules; System and File Modules; The sys Module; The os Module; The os.path module; File Pattern Matching with glob; Regular Expressions; The re Module; Match Objects; Regular Expression Objects; Special Characters; Serialization and Pickling; Unit Testing with PyUnit; Simple Tests; Tests in Groups; Embedding Jython Inside Java; Setting Up an Interpreter; Executing Code; Accessing the Interpreter Namespace; Using PyObjects; PyObject Subclasses; Catching Exceptions; Embedding Examples…”

“…It's possible with Cython, the compiler and hybrid programming language used by foundational packages such as NumPy, and prominent in projects including Pandas, h5py, and scikits-learn. …”

“…Learn how to integrate Django with pandas, NumPy, Matplotlib, and Seaborn About This Video Work with Django forms and model forms Create a modal with price statistics Transform the database and populate rows of the .csv file with sales data In Detail This course will show you how to create a professional and attractive user interface (UI) in Django for data science using the Semantic UI framework. …”

“…How to use a combination of Python Shell and PyCharm as an IDE to illustrate Python coding exercises. …”

“…Apprenez à programmer vos cartes BBC:micro bit et PyBoard Le langage MicroPython est le langage idéal pour programmer des microcontrolleurs. …”

“…In Inside Deep Learning, you will learn how to: Implement deep learning with PyTorch Select the right deep learning components Train and evaluate a deep learning model Fine tune deep learning models to maximize performance Understand deep learning terminology Adapt existing PyTorch code to solve new problems Inside Deep Learning is an accessible guide to implementing deep learning with the PyTorch framework. …”

Tabla de Contenidos: “…Las prácticas de comercio electrónico desarrolladas por las PyMEs exportadoras argentinas; 2.1. Características generales; 2.2. …”
Enlace del recurso

Tabla de Contenidos: “…Cross-Platform Data Science Applications.Appendix: Uploading Projects to PyPI…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright and Credit -- Contributors -- Table of Contents -- Preface -- Part 1: Introduction to Neural Networks -- Chapter 1: Machine Learning - an Introduction -- Technical requirements -- Introduction to ML -- Different ML approaches -- Supervised learning -- Unsupervised learning -- Reinforcement learning -- Components of an ML solution -- Neural networks -- Introducing PyTorch -- Summary -- Chapter 2: Neural Networks -- Technical requirements -- The need for NNs -- The math of NNs -- Linear algebra -- An introduction to probability -- Differential calculus -- An introduction to NNs -- Units - the smallest NN building block -- Layers as operations -- Multi-layer NNs -- Activation functions -- The universal approximation theorem -- Training NNs -- GD -- Backpropagation -- A code example of an NN for the XOR function -- Summary -- Chapter 3: Deep Learning Fundamentals -- Technical requirements -- Introduction to DL -- Fundamental DL concepts -- Feature learning -- The reasons for DL's popularity -- Deep neural networks -- Training deep neural networks -- Improved activation functions -- DNN regularization -- Applications of DL -- Introducing popular DL libraries -- Classifying digits with Keras -- Classifying digits with PyTorch -- Summary -- Part 2: Deep Neural Networks for Computer Vision -- Chapter 4: Computer Vision with Convolutional Networks -- Technical requirements -- Intuition and justification for CNNs -- Convolutional layers -- A coding example of the convolution operation -- Cross-channel and depthwise convolutions -- Stride and padding in convolutional layers -- Pooling layers -- The structure of a convolutional network -- Classifying images with PyTorch and Keras -- Convolutional layers in deep learning libraries -- Data augmentation -- Classifying images with PyTorch -- Classifying images with Keras…”

Tabla de Contenidos: “…An urgent need for efficiency in data processing -- Extracting maximum performance from built-in features -- Concurrency, parallelism, and asynchronous processing -- High-performance NumPy -- Re-implementing critical code with Cython -- Memory hierarchy, storage, and networking -- High-performance pandas and Apache arrow -- Storing big data -- Data analysis using GPU computing -- Analyzing big data with Dask…”

“…This book builds that foundation in an intuitive way-along with the PyTorch code you need to be a successful deep learning practitioner. - Vineet Gupta, Google Research A thorough explanation of the mathematics behind deep learning! …”

Tabla de Contenidos: “…Chapter 1: IoT Vulnerabilities -- Chapter 2: Classifying and Modeling IoT Behavior -- Chapter 3: Raspberry Pi Configuration and PackerRecorder.py Enhancements -- Chapter 4: Raspberry Pi as a Sensor -- Chapter 5: Operating the Raspberry Pi Sensor -- Chapter 6: Adding Finishing Touches -- Chapter 7: Future Considerations -- Appendix: Obtaining the Python Source Code -- Glossary -- …”

Tabla de Contenidos: “…; IoT reference model; IoT platforms; IoT verticals; Big data and IoT; Infusion of AI -- data science in IoT; Cross-industry standard process for data mining; AI platforms and IoT platforms; Tools used in this book; TensorFlow; Keras; Datasets; The combined cycle power plant dataset; Wine quality dataset; Air quality data; Summary; Chapter 2: Data Access and Distributed Processing for IoT; TXT format Using TXT files in PythonCSV format; Working with CSV files with the csv module; Working with CSV files with the pandas module; Working with CSV files with the NumPy module; XLSX format; Using OpenPyXl for XLSX files; Using pandas with XLSX files; Working with the JSON format; Using JSON files with the JSON module; JSON files with the pandas module; HDF5 format; Using HDF5 with PyTables; Using HDF5 with pandas; Using HDF5 with h5py; SQL data; The SQLite database engine; The MySQL database engine; NoSQL data; HDFS; Using hdfs3 with HDFS; Using PyArrow's filesystem interface for HDFS; Summary; Chapter 3: Machine Learning for IoTML and IoT; Learning paradigms; Prediction using linear regression; Electrical power output prediction using regression; Logistic regression for classification; Cross-entropy loss function; Classifying wine using logistic regressor; Classification using support vector machines; Maximum margin hyperplane; Kernel trick; Classifying wine using SVM; Naive Bayes; Gaussian Naive Bayes for wine quality; Decision trees; Decision trees in scikit; Decision trees in action; Ensemble learning; Voting classifier; Bagging and pasting; Improving your model -- tips and tricksFeature scaling to resolve uneven data scale; Overfitting; Regularization; Cross-validation; No Free Lunch theorem; Hyperparameter tuning and grid search; Summary; Chapter 4: Deep Learning for IoT; Deep learning 101; Deep learning-why now?…”