Tabla de Contenidos: “…9.1.1 Creating an SSH key pair -- 9.1.2 Enabling pubkey authentication -- 9.1.3 Tunneling through SSH -- 9.1.4 Automating an SSH tunnel with cron -- 9.2 Harvesting credentials -- 9.2.1 Harvesting credentials from bash history -- 9.2.2 Harvesting password hashes -- 9.3 Escalating privileges with SUID binaries -- 9.3.1 Locating SUID binaries with the find command -- 9.3.2 Inserting a new user into /etc/passwd -- 9.4 Passing around SSH keys -- 9.4.1 Stealing keys from a compromised host -- 9.4.2 Scanning multiple targets with Metasploit -- Summary -- 10 Controlling the entire network -- 10.1 Identifying domain admin user accounts -- 10.1.1 Using net to query Active Directory groups -- 10.1.2 Locating logged-in domain admin users -- 10.2 Obtaining domain admin privileges -- 10.2.1 Impersonating logged-in users with Incognito -- 10.2.2 Harvesting clear-text credentials with Mimikatz -- 10.3 ntds.dit and the keys to the kingdom -- 10.3.1 Bypassing restrictions with VSC -- 10.3.2 Extracting all the hashes with secretsdump.py -- Summary -- Phase 4. Documentation -- 11 Post-engagement cleanup -- 11.1 Killing active shell connections -- 11.2 Deactivating local user accounts -- 11.2.1 Removing entries from /etc/passwd -- 11.3 Removing leftover files from the filesystem -- 11.3.1 Removing Windows registry hive copies -- 11.3.2 Removing SSH key pairs -- 11.3.3 Removing ntds.dit copies -- 11.4 Reversing configuration changes -- 11.4.1 Disabling MSSQL stored procedures -- 11.4.2 Disabling anonymous file shares -- 11.4.3 Removing crontab entries -- 11.5 Closing backdoors -- 11.5.1 Undeploying WAR files from Apache Tomcat -- 11.5.2 Closing the Sticky Keys backdoor -- 11.5.3 Uninstalling persistent Meterpreter callbacks -- Summary -- 12 Writing a solid pentest deliverable -- 12.1 Eight components of a solid pentest deliverable -- 12.2 Executive summary…”

Tabla de Contenidos: “…-- 3.8.4 Listenoperationen -- 3.8.5 Projekt: Zufallsnamen -- 3.8.6 Projekt: Telefonliste -- 3.8.7 Listen durch Comprehensions erzeugen -- 3.9 Zahlen in einer Folge - range()-Funktion -- 3.10 Projekt: Klopfzeichen -- 3.11 Mengen -- 3.12 Projekt: Zufallssounds -- 3.12.1 Wie kommen Töne aus dem Raspberry Pi? -- 3.12.2 Sounds mit PyGame -- 3.12.3 Programmierung -- 3.13 Dictionaries -- 3.13.1 Operationen für Dictionaries -- 3.13.2 Projekt: Morsen -- 3.14 Aufgaben -- Kapitel 4: Funktionen -- 4.1 Aufruf von Funktionen -- 4.1.1 Unterschiedliche Anzahl von Argumenten…”

“…This experience will further help you unravel the other benefits of smart contracts, including reliable storage and backup, and efficiency. You'll also use web3.py to interact with smart contracts and leverage the power of both the web3.py and Populus framework to build decentralized applications that offer security and seamless integration with cryptocurrencies. …”

“…Since the first Hofmann-like spin crossover (SCO) behavior in {Fe(py)2[Ni(CN)4]}n (py = pyridine) was demonstrated, this crystal chemistry motif has been frequently used to design Fe(II) SCO materials to enable determination of the correlations between structural features and magnetic properties…”

“…Das Buch behandelt die Grundlagen von Python 3 und zusätzlich auch weiterführende Themen wie die Gestaltung grafischer Benutzungsoberflächen mit tkinter und PyQt, Threads und Multiprocessing, Internet-Programmierung, CGI, WSGI und Django, automatisiertes Testen, Datenmodellierung mit XML und JSON, Datenbanken, Datenvisualisierung mit Matplotlib und wissenschaftliches Rechnen mit NumPy…”

“…PyCUDA is a python library which leverages power of CUDA and GPU for accelerations. …”

“…Learn Objectives This course has extensive content that covers Python for beginners and then moves onto more complex Python operations including data analysis, exploration, and manipulation with Pandas and NumPy. It will include the following learning objectives: Work with logic in Python, assigning variables and using different data structures Create functions and classes of different types Write, run, and debug tests using Pytest to validate your work Manipulate data with Pandas Create and modify NumPy arrays Index This course is divided into content for 4 weeks, with 3 lessons per week: Week 1: Introduction to Python Reference GitHub Repository Working with variables and types Introduction to Python data structures Adding and extracting data from data structures Week 2: Python functions and Classes Reference GitHub Repository Working with functions Building classes and using methods Modules and advanced usage Week 3: Testing in Python Reference GitHub Repository Introduction to testing Writing useful tests Test failures Week 4: Introduction to Pandas and Numpy Reference GitHub Repository Basic Pandas usage Working with datasets Introduction to NumPy Resources Week 1 GitHub repository: Introduction to Python Week 2 GitHub repository: Python Functions and Classes Week 3 GitHub repository: Testing In Python Week 4 GitHub repository: Introduction to Pandas and Numpy Python dictionaries Learn Module Testing In Python book Minimal Python book Python for Beginners Learn Path Practical MLOps book…”

“…Apply mathematical fundamental principles in Python using standard mathematical libraries like NumPy and SymPy. Integrate multiple applied mathematical disciplines like linear algebra and calculus to perform tasks like gradient descent. …”

“…The lesson also covers the creation of a CUDA-enabled stress test using Rust PyTorch bindings and Rayon for multithreaded GPU-accelerated MLOps 3.0 Reproducible GitHub Codespaces repo setup for GPU workflows with Stable Diffusion with Rust PyTorch bindings 3.1 In this video, I talk about one of the most impressive use cases, @amazon #firecracker that powers #aws #lambda. …”

“…You'll balance both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and NumPy. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic ..…”

“…You'll start by developing core skills and learning about packages covered in Python's scientific stack, including NumPy, SciPy, and Matplotlib. As you advance, you'll get to grips with more advanced topics of calculus, probability, and networks (graph theory). …”

“…Building a CLI is the foundation for automation in your daily work By the end of the course you should feel confident in creating a tool, and the following: Create simple CLI tools without any frameworks Learn about arguments, flags, help menus and how to create them automatically Use the argparse framework to build more complex tools Build a CLI with the Click framework Use special features of Click like colored output and argument types Modularizing and project scaffolding in Python Packaging and packaging files in Python How to create tests and run them automatically Continuous Integration and Continuous Deployment with Github Actions for Python Packaging a CLI tool with an executable Distributing a CLI tool on PyPI (the Python Package Index) How to automatically test your project on a PR (GitHub pull request) How to automate publishing your tool to PyPI on a release We'll cover 3 different ways to create tools, from the very basic (with sys.argv) to using a framework that comes with Python ( argparse), and finally the more involved, by using an external library like Click. …”

“…Later, you'll work with ML and DL models using TensorFlow and PyTorch. Finally, you'll learn how to evaluate, compare, optimize models, and more using the recipes covered in the book. …”

“…Following this, you'll learn how to build an advanced chatbot, and scale things up using PySpark. In the concluding chapters, you can look forward to exciting insights into deep learning and you'll even create an application using computer vision and neural networks. …”

“…At the start, you'll learn the basics of Python 3, and the fundamentals of single-board computers and NumPy. Next, you'll discover how to install OpenCV 4 for Python 3 on Raspberry Pi, before covering major techniques and algorithms in image processing, manipulation, and computer vision. …”

“…Learning objectives Find and use pre-trained models Fine-tune models for custom tasks Build ML pipelines with Hugging Face libraries Create and version datasets Containerize models for production Automate workflows for MLOps Lesson 1: Getting Started with Hugging Face Lesson Outline Overview of Hugging Face Hub Browsing models and datasets Using Hugging Face repositories Managing spaces and access Lesson 2: Applying Hugging Face Models Lesson Outline Downloading models from the Hub Using models with PyTorch/TensorFlow Leveraging tokenizers and pipelines Performing inference with Hub models Lesson 3: Working with Datasets Lesson Outline Browsing datasets on Hugging Face Uploading and managing datasets Versioning datasets with dataset cards Loading datasets in PyTorch/TensorFlow Lesson 4: Model Serving and Deployment Lesson Outline Containerizing Hugging Face models Creating inference APIs with FastAPI Deploying to cloud services like Azure Automating with GitHub Actions About your instructor Alfredo Deza has over a decade of experience as a Software Engineer doing DevOps, automation, and scalable system architecture. …”

“…What you will learn Master foundational concepts that underpin all data science applications Use advanced techniques to elevate your data science proficiency Apply data science concepts to solve real-world data science challenges Implement the NumPy, SciPy, and scikit-learn concepts in Python Build predictive machine learning models with mathematical concepts Gain expertise in Bayesian non-parametric methods for advanced probabilistic modeling Acquire mathematical skills tailored for time-series and network data types Who this book is for This book is for data scientists, machine learning engineers, and data analysts who already use data science tools and libraries but want to learn more about the underlying math. …”

“…This course includes GitHub repositories you can reference: Python CLI Examples Basic Python CLI Web API with Python and Hugging Face models Learn objectives Consume and use APIs and SDKs in Python Create command-line programs for automation Develop web services and APIs with Python frameworks Package Python projects for distribution Apply skills to build useful interfaces for ML models Lesson 1: Working with APIs in Python Lesson Outline Overview of APIs and SDKs Using the Requests Library Consuming REST APIs Python SDKs like NumPy and SciPy Project: Building a Python script using APIs Lesson 2: Building Command-line Interfaces Lesson Outline Intro to automation with CLI tools Parsing arguments and options Organizing code into modules Python packaging for distribution Project: CLI tool for machine learning Lesson 3: Developing Web APIs Lesson Outline REST API concepts Web frameworks like Flask and FastAPI Building an API with Flask Developing APIs with FastAPI OpenAPI specs and documentation Project: Web API for movie recommendations About your instructor Alfredo Deza has over a decade of experience as a Software Engineer doing DevOps, automation, and scalable system architecture. …”

“…By the end of this book, you'll have learned to create graph datasets, implement graph neural networks using Python and PyTorch Geometric, and apply them to solve real-world problems, along with building and training graph neural network models for node and graph classification, link prediction, and much more…”