“…You’ll also learn how to: •Leverage data structures to solve real-world problems, like using Boolean indexing to find cities with above-average pollution •Use NumPy basics such as array, shape, axis, type, broadcasting, advanced indexing, slicing, sorting, searching, aggregating, and statistics •Calculate basic statistics of multidimensional data arrays and the K-Means algorithms for unsupervised learning •Create more advanced regular expressions using grouping and named groups, negative lookaheads, escaped characters, whitespaces, character sets (and negative characters sets), and greedy/nongreedy operators •Understand a wide range of computer science topics, including anagrams, palindromes, supersets, permutations, factorials, prime numbers, Fibonacci numbers, obfuscation, searching, and algorithmic sorting By the end of the book, you’ll know how to write Python at its most refined, and create concise, beautiful pieces of ""Python art"" in merely a single line…”

“…In addition, there is comprehensive coverage of Spark, PySpark, and the Zeppelin web-GUI. The steps for easily installing a working Hadoop/Spark system on a desktop/laptop and on a local stand-alone cluster using the powerful Ambari GUI are also included. …”

“…Become familiar with various AI offerings and capabilities Build intelligent applications using Azure Cognitive Services Train, tune, and deploy models with Azure Machine Learning, PyTorch, and the Open Neural Network Exchange (ONNX) Learn about how companies have used Cognitive Services to solve business problems Use transfer learning to train vision, speech, and language models in minutes Discover how Microsoft scaled running Azure Cognitive Services for millions of users…”

“…What's Inside The deep learning development cycle Automate training in TensorFlow and PyTorch Dataset management, model serving, and hyperparameter tuning A hands-on deep learning lab About the Reader For software developers and engineering-minded data scientists. …”

“…Ce livre décrit toutes les ressources dont vous pouvez disposer pour mettre en oeuvre vos applications : Python, NumPy, Pandas, Matplotlib, Scikit-Learn et d'autres outils associés. …”

“…Python, together with Pygame and PyOpenGL, provides you with the opportunity to explore these features under the hood, revealing how computers generate and manipulate 3D environments…”

Tabla de Contenidos: “…-- Python installation -- Installing the PyCharm IDE -- Setting up a Python project inside PyCharm -- Exploring some nifty PyCharm features -- Code debugging -- Code refactoring -- Installing packages from the GUI -- Summary -- Chapter 2: Common Libraries Used in Automation -- Understanding Python packages -- Package search paths -- Common Python libraries -- Network Python Libraries -- System and cloud Python libraries -- Accessing module source code -- Visualizing Python code -- Summary -- Chapter 3: Setting Up the Network Lab Environment -- Technical requirements -- When and why to automate the network -- Why do we need automation? …”

Tabla de Contenidos: “…Data modeling for infrastructure as code -- The Cisco API and ACI -- Cisco NX-API -- Lab Software Installation and Device Preparation -- NX-API examples -- Cisco and YANG model -- 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 -- The Arista eAPI management -- The eAPI preparation -- eAPI examples -- The Arista Pyeapi library -- The Pyeapi installation -- Pyeapi examples -- Vendor neutral libraries -- Summary -- Chapter 4: The Python Automation Framework - Ansible Basics -- A quick Ansible example -- The control node installation -- Your first Ansible playbook -- The Public key authorization -- The inventory file -- Our first playbook -- The advantages of Ansible -- Agentless -- Idempotent -- Simple and extensible -- The vendor Support -- The Ansible architecture -- YAML -- Inventories -- Variables -- Templates with Jinja2 -- Ansible networking modules -- Local connections and facts -- Provider arguments -- The Ansible Cisco example -- The Ansible Juniper example -- The Ansible Arista example -- Summary -- Chapter 5: The Python Automation Framework - Ansible Advance Topics -- Ansible conditionals -- The when clause -- 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 -- The lab setup -- Python Scapy -- Installing Scapy -- Interactive examples…”

Tabla de Contenidos: “…Classes -- Rcpp Modules -- Testing -- Measuring Performance -- Debugging -- Distribution Explorer -- Summary -- Additional Resources -- Exercises -- Part IV: Python -- Chapter 7: Building a Python Extension Module -- Introduction -- Prerequisites -- Using Visual Studio Community Edition 2019 -- StatsPythonRaw -- Project Settings -- Code Organization -- Functions -- Declarations -- Descriptive Statistics -- Linear Regression -- Statistical Tests -- The Conversion Layer -- Error Handling -- The Module Definition -- Python Client -- Debugging -- Summary -- Additional Resources -- Exercises -- Chapter 8: Module Development with Boost.Python and PyBind -- Introduction -- Boost.Python -- Prerequisites -- Project Settings -- Code Organization -- Functions -- StatisticalTests -- The Conversion Layer -- The Module Definition -- Exception Handling -- PyBind -- Prerequisites -- Project Settings -- Code Organization: module.cpp -- Exception Handling -- The Python "Client" -- Performance -- The Statistics Service -- Summary -- Additional Resources -- Exercises…”

Tabla de Contenidos: “…[Python multiprocessing and the GIL] -- Python multiprocessing and the GIL -- Summary -- Chapter 5: Dynamic Content -- An example dynamic web page -- Reverse engineering a dynamic web page -- Edge cases -- Rendering a dynamic web page -- PyQt or PySide -- Debugging with Qt -- Executing JavaScript -- Website interaction with WebKit -- Waiting for results -- The Render class -- Selenium -- Selenium and Headless Browsers -- Summary -- Chapter 6: Interacting with Forms -- The Login form -- Loading cookies from the web browser -- Extending the login script to update content -- Automating forms with Selenium -- -- Summary -- Chapter 7: Solving CAPTCHA -- Registering an account -- Loading the CAPTCHA image -- Optical character recognition -- Further improvements -- Solving complex CAPTCHAs -- Using a CAPTCHA solving service -- Getting started with 9kw -- The 9kw CAPTCHA API -- Reporting errors -- Integrating with registration -- CAPTCHAs and machine learning -- Summary -- Chapter 8: Scrapy -- Installing Scrapy -- Starting a project -- Defining a model -- Creating a spider -- Tuning settings -- Testing the spider -- Different Spider Types -- Scraping with the shell command -- Checking results -- Interrupting and resuming a crawl -- Scrapy Performance Tuning -- Visual scraping with Portia -- Installation -- Annotation -- Running the Spider -- Checking results -- Automated scraping with Scrapely -- Summary -- Chapter 9: Putting It All Together -- Google search engine -- Facebook -- The website -- Facebook API -- Gap -- BMW -- Summary -- Index…”

Tabla de Contenidos: “…Cover -- Copyright -- Contributors -- Table of Contents -- Preface -- Chapter 1: Getting Started with Machine Learning and Python -- An introduction to machine learning -- Understanding why we need machine learning -- Differentiating between machine learning and automation -- Machine learning applications -- Knowing the prerequisites -- Getting started with three types of machine learning -- A brief history of the development of machine learning algorithms -- Digging into the core of machine learning -- Generalizing with data -- Overfitting, underfitting, and the bias-variance trade-off -- Overfitting -- Underfitting -- The bias-variance trade-off -- Avoiding overfitting with cross-validation -- Avoiding overfitting with regularization -- Avoiding overfitting with feature selection and dimensionality reduction -- Data preprocessing and feature engineering -- Preprocessing and exploration -- Dealing with missing values -- Label encoding -- One-hot encoding -- Dense embedding -- Scaling -- Feature engineering -- Polynomial transformation -- Binning -- Combining models -- Voting and averaging -- Bagging -- Boosting -- Stacking -- Installing software and setting up -- Setting up Python and environments -- Installing the main Python packages -- NumPy -- SciPy -- pandas -- scikit-learn -- TensorFlow -- PyTorch -- Summary -- Exercises -- Chapter 2: Building a Movie Recommendation Engine with Naïve Bayes -- Getting started with classification -- Binary classification -- Multiclass classification -- Multi-label classification -- Exploring Naïve Bayes -- Bayes' theorem by example -- The mechanics of Naïve Bayes -- Implementing Naïve Bayes -- Implementing Naïve Bayes from scratch -- Implementing Naïve Bayes with scikit-learn -- Building a movie recommender with Naïve Bayes -- Preparing the data -- Training a Naïve Bayes model…”

Tabla de Contenidos: “…-- Using DataFrames with MLlib -- Examining the spark-linear-regression.py script -- Getting results -- Spark Streaming and GraphX -- What is Spark Streaming? …”

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