Presentation used for tutorial session on Python for finalists of CSEA Code Maestros on Feb 11, 2012. More resources at http://athena.nitc.ac.in/~k4rtik/python/
Python offers several tool and public services that simplify starting and maintaining an open source project. This presentation show cases some of the most helpful one and explains the process, beginning with an empty folder and finishing with a published PyPI package.
Go is an open source programming language designed by Google to be concurrent, garbage collected, and efficient. It has a simple syntax and is used by Google and others to build large distributed systems. Key features include garbage collection, concurrency with goroutines and channels, interfaces without inheritance, and a large standard library.
This document discusses the padding oracle attack, which allows decryption of encrypted data by exploiting flaws in padding validation on encrypted ciphertext. It describes how the attack works by using a padding validation "oracle" to decrypt ciphertext blocks one-by-one. It then explains how this can be used to decrypt web traffic and authentication cookies, potentially allowing complete compromise of the system.
The document discusses why Python can be slow compared to other languages and provides tips for optimizing Python code. It explains that Python is an interpreted language and lacks type safety, which contributes to slower performance than compiled languages. However, Python includes tools like NumPy that optimize certain operations. The document recommends profiling code to identify bottlenecks, using appropriate data structures and algorithms, minimizing interpreter overhead through concurrency or C extensions, and as a last resort considering other languages. Overall it provides guidance on architectural choices, algorithms, memory usage, and language options to improve Python performance.
The document discusses improving foreign function interface (FFI) techniques in Smalltalk by making them more portable across implementations. It proposes extending the interpreter to allow direct calls to C functions, similar to approaches used in Python, Lua, and .NET. This would involve adding primitives for basic CPU types to the bytecode and implementing the interface in plugins for different backends like C, C++, and a virtual CPU.
Go was created at Google to address needs for efficient large-scale programming, fast compilation, distributed systems, multicore hardware, and networked computing. It is a concurrent and garbage-collected systems programming language. A simple web server can be written in Go with just a few lines of code. Go uses communicating sequential processes as its concurrency model and has interfaces but no inheritance. Concurrency is achieved through communicating rather than sharing memory. Parallelism is easy to implement using goroutines, channels, locks, or the once package.
Python offers several tool and public services that simplify starting and maintaining an open source project. This presentation show cases some of the most helpful one and explains the process, beginning with an empty folder and finishing with a published PyPI package.
Go is an open source programming language designed by Google to be concurrent, garbage collected, and efficient. It has a simple syntax and is used by Google and others to build large distributed systems. Key features include garbage collection, concurrency with goroutines and channels, interfaces without inheritance, and a large standard library.
This document discusses the padding oracle attack, which allows decryption of encrypted data by exploiting flaws in padding validation on encrypted ciphertext. It describes how the attack works by using a padding validation "oracle" to decrypt ciphertext blocks one-by-one. It then explains how this can be used to decrypt web traffic and authentication cookies, potentially allowing complete compromise of the system.
The document discusses why Python can be slow compared to other languages and provides tips for optimizing Python code. It explains that Python is an interpreted language and lacks type safety, which contributes to slower performance than compiled languages. However, Python includes tools like NumPy that optimize certain operations. The document recommends profiling code to identify bottlenecks, using appropriate data structures and algorithms, minimizing interpreter overhead through concurrency or C extensions, and as a last resort considering other languages. Overall it provides guidance on architectural choices, algorithms, memory usage, and language options to improve Python performance.
The document discusses improving foreign function interface (FFI) techniques in Smalltalk by making them more portable across implementations. It proposes extending the interpreter to allow direct calls to C functions, similar to approaches used in Python, Lua, and .NET. This would involve adding primitives for basic CPU types to the bytecode and implementing the interface in plugins for different backends like C, C++, and a virtual CPU.
Go was created at Google to address needs for efficient large-scale programming, fast compilation, distributed systems, multicore hardware, and networked computing. It is a concurrent and garbage-collected systems programming language. A simple web server can be written in Go with just a few lines of code. Go uses communicating sequential processes as its concurrency model and has interfaces but no inheritance. Concurrency is achieved through communicating rather than sharing memory. Parallelism is easy to implement using goroutines, channels, locks, or the once package.
Introduction to python programming, Why Python?, Applications of PythonPro Guide
Python is a high-level, general-purpose programming language created in 1991. It is used for web development through frameworks like Django and Flask, game development using PySoy and PyGame, artificial intelligence and machine learning through various open-source libraries, and desktop GUI applications with toolkits like PyQt and PyGtk. Python code is often more concise and readable than other languages due to its simple English-like syntax and ability to run on many platforms including Windows, Mac, Linux and Raspberry Pi.
Writing Fast Code (JP) - PyCon JP 2015Younggun Kim
The document discusses optimizing Python code performance through profiling. It introduces various profiling tools like cProfile and line_profiler. As an example, it profiles a "fibonachicken" function that uses Fibonacci numbers to calculate the number of chickens needed to serve a given number of people. Profiling reveals the fib() and is_fibonacci() functions as bottlenecks. The document suggests improving fib() with Binet's formula and is_fibonacci() with Gessel's formula to avoid using fib() and gain better performance.
GCC is a widely used open source compiler system developed by the GNU Project. It compiles C, C++, Java, Fortran and other languages. GCC has undergone major changes to its structure since 2005, including the addition of GENERIC and GIMPLE intermediate representations between the front end and back end. The front end parses source code into ASTs, then GIMPLE trees are optimized through many passes in the middle end before being converted to RTL for the back end code generation.
When working with big data or complex algorithms, we often look to parallelize our code to optimize runtime. By taking advantage of a GPUs 1000+ cores, a data scientist can quickly scale out solutions inexpensively and sometime more quickly than using traditional CPU cluster computing. In this webinar, we will present ways to incorporate GPU computing to complete computationally intensive tasks in both Python and R.
See the full presentation here: 👉 https://vimeo.com/153290051
Learn more about the Domino data science platform: https://www.dominodatalab.com
This document discusses using profilers to optimize code performance. It introduces common Python profilers like cProfile and line_profiler. As an example, it profiles a fibonachicken.py code that calculates the number of chickens needed based on fibonacci numbers. Both the fib() and is_fibonacci() functions were bottlenecks. Two hypotheses for improvement were tested: 1) optimizing fib() using Binet's formula, and 2) improving is_fibonacci() to not use fib() by using Gessel's formula instead. Profiling confirmed the optimizations were effective. The document emphasizes considering code efficiency along with system details and circumstances to identify optimization opportunities.
GoLang is an open source programming language created by Google in 2009. It has a large community and was designed for scalability and concurrency. Some key features include being statically typed, compiled, and having built-in support for concurrency through goroutines and channels. Google uses GoLang extensively to build systems that scale to thousands of machines.
The speaker discussed the benefits of type hints in Python. Type hints allow specifying the expected types of function parameters and return values, improving code readability, enabling code completion in editors, and allowing static type checking tools to analyze the code for type errors. The speaker demonstrated how to write type hints according to PEP 484 and PEP 526 standards and how to retrieve type information. Tools like Mypy were presented for doing static type analysis to catch errors. Using type hints and type checkers in continuous integration was recommended to catch errors early when collaborating on projects. The speaker concluded by explaining how using type hints made it easier for their team to port code from Python 2 to Python 3.
Even though Python allows many ways to easily debug and profile your code, it is not uncommon to see people overusing simple print statements for this. The presentation will provide an overview of the most common basic debugging techniques that every Python programmer should know. Additionally, for debugging speed or memory problems, couple profilers are presented. Outline:
Basic techniques (print statements, logging)
Debuggers (pdb, winpdb/rpdb2)
Profiling (cProfile, guppy, ...)
This document discusses getting started with a first Python project. It covers installing Python and choosing an IDE, following coding best practices like PEP8 style guidelines, using built-in data structures, testing tools, virtual environments, project structure, and deployment tools like Supervisor. The goal is to help new Python programmers understand the basics of starting their first project.
This document provides an introduction to programming in Go. It discusses the origins and intentions of the Go language, where it is commonly used today, and what Go is and isn't. Go was created to be a systems programming language with better productivity than C++. It has seen widespread adoption beyond its original use cases. While Go isn't a functional or object-oriented language, it is compiled, statically typed, memory managed, concurrent, and ideal for building cloud infrastructure. The document also covers Go syntax including variables, types, loops, conditionals, functions, and more.
This document introduces Python and discusses its main features and advantages over other languages like Java. Python is described as a high-level, multi-paradigm language with simple yet powerful semantics and a focus on productivity. It discusses how Python code is more concise, readable and fun to write compared to Java, C#, and other languages. Python trusts the programmer and aims to avoid getting in the way. It also has a rich standard library and ecosystem of third-party libraries.
Presentation of Python, Django, DockerStackDavid Sanchez
Python is a widely used high-level, general-purpose, interpreted programming language. It provides constructs intended to enable clear programs on both small and large scale. Python features a dynamic type system and automatic memory management and supports multiple programming paradigms, including object-oriented, imperative, functional and procedural styles. It has a large and comprehensive standard library.
This document provides an overview of the GCC compiler, including its history and development by Richard Stallman as part of the GNU project in 1984. It describes how GCC can be used to compile C and C++ code and supports cross-compiling for different architectures. The document outlines several common GCC options such as -c to compile only, -o to specify an output file name, -g to include debugging information, -Wall to show warnings, and -ansi to ensure standard compliance.
This document introduces Protocol Buffers, an efficient data interchange format developed by Google. It allows data to be serialized and deserialized between different programming languages like Java, Python, and C++. Protocol Buffers use an Interface Definition Language (IDL) to define how serialized data is structured. Once defined, code can be generated to easily read and write protocol buffer data to and from various applications.
The GNOME way - What can we learn from and within the Open Documentation WorldRadina Matic
The presentation gives an overview of the documentation for the GNOME desktop environment including the processes of user and developer help creation, review, release and bug tracking; documentation team management; collaboration with design, usability and localization teams and respective workflows; change management (DocBook to Mallard). The second part of the session presents the value of the free and open-source platforms like GNOME, as a real-world practice-playground resource for technical communication students, trainees and trainers.
Presented at tcworld 2014 conference in Stuttgart, November 2014.
There are two videos by Bastian Ilsø from GNOMEDesktop (https://www.youtube.com/user/GNOMEDesktop/) integrated into the presentation that I showed at the conference:
Introducing GNOME 3.14 - https://www.youtube.com/watch?v=7p8Prlu3owc
Discover GNOME’s Docs - https://www.youtube.com/watch?v=dCu3Ww8iI3Y
a presentation about python programming language made and presented by me in a lecture to show the importance of python in the real world to my colleagues
This document provides an overview of the C programming language under Linux, covering preprocessing, compiling, assembling, linking, libraries, and related tools. It discusses:
1. The four main steps of preprocessing, compiling, assembling, and linking using GNU tools like cpp, cc1, as, and collect2.
2. How to display symbol tables using nm and strip symbols from executables.
3. Creating static libraries with ar and ranlib and linking them, as well as creating shared libraries with gcc and soname.
4. The roles of environment variables like LIBRARY_PATH and LD_LIBRARY_PATH in locating libraries.
Introduction to go language programming , benchmark with another language programming nodejs , php , ruby & python . how install go . use what IDE . and rapid learnin golang
PyCon 2013 : Scripting to PyPi to GitHub and MoreMatt Harrison
This document discusses various aspects of developing and distributing Python projects, including versioning, configuration, logging, file input, shell invocation, environment layout, project layout, documentation, automation with Makefiles, packaging, testing, GitHub, Travis CI, and PyPI. It recommends using semantic versioning, the logging module, parsing files with the file object interface, invoking shell commands with subprocess, using virtualenv for sandboxed environments, Sphinx for documentation, Makefiles to automate tasks, setuptools for packaging, and GitHub, Travis CI and PyPI for distribution.
Introduction to python programming, Why Python?, Applications of PythonPro Guide
Python is a high-level, general-purpose programming language created in 1991. It is used for web development through frameworks like Django and Flask, game development using PySoy and PyGame, artificial intelligence and machine learning through various open-source libraries, and desktop GUI applications with toolkits like PyQt and PyGtk. Python code is often more concise and readable than other languages due to its simple English-like syntax and ability to run on many platforms including Windows, Mac, Linux and Raspberry Pi.
Writing Fast Code (JP) - PyCon JP 2015Younggun Kim
The document discusses optimizing Python code performance through profiling. It introduces various profiling tools like cProfile and line_profiler. As an example, it profiles a "fibonachicken" function that uses Fibonacci numbers to calculate the number of chickens needed to serve a given number of people. Profiling reveals the fib() and is_fibonacci() functions as bottlenecks. The document suggests improving fib() with Binet's formula and is_fibonacci() with Gessel's formula to avoid using fib() and gain better performance.
GCC is a widely used open source compiler system developed by the GNU Project. It compiles C, C++, Java, Fortran and other languages. GCC has undergone major changes to its structure since 2005, including the addition of GENERIC and GIMPLE intermediate representations between the front end and back end. The front end parses source code into ASTs, then GIMPLE trees are optimized through many passes in the middle end before being converted to RTL for the back end code generation.
When working with big data or complex algorithms, we often look to parallelize our code to optimize runtime. By taking advantage of a GPUs 1000+ cores, a data scientist can quickly scale out solutions inexpensively and sometime more quickly than using traditional CPU cluster computing. In this webinar, we will present ways to incorporate GPU computing to complete computationally intensive tasks in both Python and R.
See the full presentation here: 👉 https://vimeo.com/153290051
Learn more about the Domino data science platform: https://www.dominodatalab.com
This document discusses using profilers to optimize code performance. It introduces common Python profilers like cProfile and line_profiler. As an example, it profiles a fibonachicken.py code that calculates the number of chickens needed based on fibonacci numbers. Both the fib() and is_fibonacci() functions were bottlenecks. Two hypotheses for improvement were tested: 1) optimizing fib() using Binet's formula, and 2) improving is_fibonacci() to not use fib() by using Gessel's formula instead. Profiling confirmed the optimizations were effective. The document emphasizes considering code efficiency along with system details and circumstances to identify optimization opportunities.
GoLang is an open source programming language created by Google in 2009. It has a large community and was designed for scalability and concurrency. Some key features include being statically typed, compiled, and having built-in support for concurrency through goroutines and channels. Google uses GoLang extensively to build systems that scale to thousands of machines.
The speaker discussed the benefits of type hints in Python. Type hints allow specifying the expected types of function parameters and return values, improving code readability, enabling code completion in editors, and allowing static type checking tools to analyze the code for type errors. The speaker demonstrated how to write type hints according to PEP 484 and PEP 526 standards and how to retrieve type information. Tools like Mypy were presented for doing static type analysis to catch errors. Using type hints and type checkers in continuous integration was recommended to catch errors early when collaborating on projects. The speaker concluded by explaining how using type hints made it easier for their team to port code from Python 2 to Python 3.
Even though Python allows many ways to easily debug and profile your code, it is not uncommon to see people overusing simple print statements for this. The presentation will provide an overview of the most common basic debugging techniques that every Python programmer should know. Additionally, for debugging speed or memory problems, couple profilers are presented. Outline:
Basic techniques (print statements, logging)
Debuggers (pdb, winpdb/rpdb2)
Profiling (cProfile, guppy, ...)
This document discusses getting started with a first Python project. It covers installing Python and choosing an IDE, following coding best practices like PEP8 style guidelines, using built-in data structures, testing tools, virtual environments, project structure, and deployment tools like Supervisor. The goal is to help new Python programmers understand the basics of starting their first project.
This document provides an introduction to programming in Go. It discusses the origins and intentions of the Go language, where it is commonly used today, and what Go is and isn't. Go was created to be a systems programming language with better productivity than C++. It has seen widespread adoption beyond its original use cases. While Go isn't a functional or object-oriented language, it is compiled, statically typed, memory managed, concurrent, and ideal for building cloud infrastructure. The document also covers Go syntax including variables, types, loops, conditionals, functions, and more.
This document introduces Python and discusses its main features and advantages over other languages like Java. Python is described as a high-level, multi-paradigm language with simple yet powerful semantics and a focus on productivity. It discusses how Python code is more concise, readable and fun to write compared to Java, C#, and other languages. Python trusts the programmer and aims to avoid getting in the way. It also has a rich standard library and ecosystem of third-party libraries.
Presentation of Python, Django, DockerStackDavid Sanchez
Python is a widely used high-level, general-purpose, interpreted programming language. It provides constructs intended to enable clear programs on both small and large scale. Python features a dynamic type system and automatic memory management and supports multiple programming paradigms, including object-oriented, imperative, functional and procedural styles. It has a large and comprehensive standard library.
This document provides an overview of the GCC compiler, including its history and development by Richard Stallman as part of the GNU project in 1984. It describes how GCC can be used to compile C and C++ code and supports cross-compiling for different architectures. The document outlines several common GCC options such as -c to compile only, -o to specify an output file name, -g to include debugging information, -Wall to show warnings, and -ansi to ensure standard compliance.
This document introduces Protocol Buffers, an efficient data interchange format developed by Google. It allows data to be serialized and deserialized between different programming languages like Java, Python, and C++. Protocol Buffers use an Interface Definition Language (IDL) to define how serialized data is structured. Once defined, code can be generated to easily read and write protocol buffer data to and from various applications.
The GNOME way - What can we learn from and within the Open Documentation WorldRadina Matic
The presentation gives an overview of the documentation for the GNOME desktop environment including the processes of user and developer help creation, review, release and bug tracking; documentation team management; collaboration with design, usability and localization teams and respective workflows; change management (DocBook to Mallard). The second part of the session presents the value of the free and open-source platforms like GNOME, as a real-world practice-playground resource for technical communication students, trainees and trainers.
Presented at tcworld 2014 conference in Stuttgart, November 2014.
There are two videos by Bastian Ilsø from GNOMEDesktop (https://www.youtube.com/user/GNOMEDesktop/) integrated into the presentation that I showed at the conference:
Introducing GNOME 3.14 - https://www.youtube.com/watch?v=7p8Prlu3owc
Discover GNOME’s Docs - https://www.youtube.com/watch?v=dCu3Ww8iI3Y
a presentation about python programming language made and presented by me in a lecture to show the importance of python in the real world to my colleagues
This document provides an overview of the C programming language under Linux, covering preprocessing, compiling, assembling, linking, libraries, and related tools. It discusses:
1. The four main steps of preprocessing, compiling, assembling, and linking using GNU tools like cpp, cc1, as, and collect2.
2. How to display symbol tables using nm and strip symbols from executables.
3. Creating static libraries with ar and ranlib and linking them, as well as creating shared libraries with gcc and soname.
4. The roles of environment variables like LIBRARY_PATH and LD_LIBRARY_PATH in locating libraries.
Introduction to go language programming , benchmark with another language programming nodejs , php , ruby & python . how install go . use what IDE . and rapid learnin golang
PyCon 2013 : Scripting to PyPi to GitHub and MoreMatt Harrison
This document discusses various aspects of developing and distributing Python projects, including versioning, configuration, logging, file input, shell invocation, environment layout, project layout, documentation, automation with Makefiles, packaging, testing, GitHub, Travis CI, and PyPI. It recommends using semantic versioning, the logging module, parsing files with the file object interface, invoking shell commands with subprocess, using virtualenv for sandboxed environments, Sphinx for documentation, Makefiles to automate tasks, setuptools for packaging, and GitHub, Travis CI and PyPI for distribution.
Python is a popular programming language created by Guido van Rossum in 1991. It is easy to use, powerful, and versatile, making it suitable for beginners and experts alike. Python code can be written and executed in the browser using Google Colab, which provides a Jupyter notebook environment and access to computing resources like GPUs. The document then discusses installing Python using Anaconda, basic Python concepts like indentation, variables, strings, conditionals, and loops.
This document provides an introduction and overview of using Python on the Raspberry Pi. It discusses that Python is a general purpose language created in the late 1980s that is supported on many operating systems and hardware, including the Raspberry Pi. It then provides tips and recommendations for learning Python, using popular Python libraries, virtual environments, best coding practices, and web development frameworks. Specific libraries and tools mentioned include IPython, Requests, Pandas, Matplotlib, Scikit-Learn, Bottle, Flask, and Django. Source code examples are also included.
This document provides an overview of the Python programming language, including its history, key features, and common uses. It discusses how Python is an interpreted, object-oriented language with dynamic typing and automatic memory management. Examples are given of Python's syntax for numbers, strings, modules, data structures like lists and dictionaries, and the interactive shell. Popular applications of Python like web development, science, and games are also mentioned.
Try to imagine the amount of time and effort it would take you to write a bug-free script or application that will accept a URL, port scan it, and for each HTTP service that it finds, it will create a new thread and perform a black box penetration testing while impersonating a Blackberry 9900 smartphone. While you’re thinking, Here’s how you would have done it in Hackersh:
“http://localhost” \
-> url \
-> nmap \
-> browse(ua=”Mozilla/5.0 (BlackBerry; U; BlackBerry 9900; en) AppleWebKit/534.11+ (KHTML, like Gecko) Version/7.1.0.346 Mobile Safari/534.11+”) \
-> w3af
Meet Hackersh (“Hacker Shell”) – A new, free and open source cross-platform shell (command interpreter) with built-in security commands and Pythonect-like syntax.
Aside from being interactive, Hackersh is also scriptable with Pythonect. Pythonect is a new, free, and open source general-purpose dataflow programming language based on Python, written in Python. Hackersh is inspired by Unix pipeline, but takes it a step forward by including built-in features like remote invocation and threads. This 120 minute lab session will introduce Hackersh, the automation gap it fills, and its features. Lots of demonstrations and scripts are included to showcase concepts and ideas.
This document introduces Python and provides an overview of its key features. It discusses Python's history and design philosophy, covers basic syntax like variables, expressions, conditionals and loops. It also summarizes Python's core datatypes like strings, lists, dictionaries and files. The document is intended to give readers a high-level understanding of Python for the purposes of an introductory talk or seminar on the language.
This document discusses SWIG (Simplified Wrapper and Interface Generator), which is a tool that takes C/C++ declarations as input and generates bindings to other languages like Python, Tcl, Perl, and Guile. SWIG allows functions, variables, constants, and C++ classes to be accessed from these scripting languages. It handles data type conversions and run-time type checking. The document provides examples of using SWIG to expose a simple C function and C++ class to Python.
The document discusses best practices for writing a C/C++ Python extension in 2017. It covers available options like ctypes, cffi, Cython, and SWIG. It then focuses on building a binary Python extension using ctypes, including debugging crashes by generating core files and using lldb/gdb. It also discusses memory issues and using valgrind and clang sanitizers. It recommends abusing Python unit tests for testing C code. Finally, it covers shipping the extension, including manylinux wheels, testing wheels on different Linux distributions with Docker, and publishing source and wheel distributions.
This document provides an overview of several technologies: Git for version control, Python as a programming language, Django as a web framework built with Python, and Heroku as a platform for deploying Python/Django apps. It discusses basic Git commands and workflows. It also introduces Python concepts like shells, modules, and virtual environments. Django fundamentals like the MVT pattern and templates are covered. The document recommends Python 2.7 for Django projects and provides sample code.
carrow - Go bindings to Apache Arrow via C++-APIYoni Davidson
Apache Arrow is a cross-language development platform for in-memory data that specifies a standardized columnar memory format. It provides libraries and messaging for moving data between languages and services without serialization. The presenter discusses their motivation for creating Go bindings for Apache Arrow via C++ to share data between Go and Python programs using the same memory format. They explain several challenges of this approach, such as different memory managers in Go and C++, and solutions like generating wrapper code and handling memory with finalizers.
What is Python? (Silicon Valley CodeCamp 2015)wesley chun
Slide deck for the 45-60-minute introduction to Python session talk delivered at Silicon Valley CodeCamp 2015: https://www.siliconvalley-codecamp.com/Session/2015/what-is-python
ABSTRACT
Python is an agile object-oriented programming language that continues to build momentum. It can do everything Java, C/C++/C#, Ruby, PHP, and Perl can do, but it's also fun & intuitive! Enjoy coding as fast as you think with a simple yet robust syntax that encourages group collaboration. It is known for several popular web frameworks, including Django (Python's equivalent to Ruby on Rails), Pyramid, and web2py. There is also Google App Engine, where Python was the first supported runtime. Users supporting Zope, Plone, Trac, and Mailman will also benefit from knowing some Python. Python can do XML/ReST/XSLT, multithreading, SQL/databases, GUIs, hardcore math/science, Internet client/server systems & networking (heard of Twisted?), GIS/ESRI, QA/test, automation frameworks, plus system administration tasks too! On the education front, it's a great tool to teach programming with (especially those who have done Scratch or Tynker already) as well as a solid (first) language to learn for non-programmers and other technical staff. Finally, if Python doesn't do what you want, you can extend it in C/C++, Java, or C# (even VB.NET)! Have you noticed the huge growth in the number of jobs on Monster & Dice that list Python as a desired skill? Come find out why Google, Yahoo!, Disney, Cisco, YouTube, LinkedIn, Yelp, LucasFilm/ILM, Pixar, NASA, Ubuntu, Bank of America, and Red Hat all use Python!
This document provides an introduction to the Python programming language. It discusses what Python is, why it was created, its basic features and uses. Python is an interpreted, object-oriented programming language that is designed to be readable. It can be used for tasks such as web development, scientific computing, and scripting. The document also covers Python basics like variables, data types, operators, and input/output functions. It provides examples of Python code and discusses best practices for writing and running Python programs.
This document provides an introduction to the Python programming language. It begins by asking why Python is needed after showing a simple "Hello World" program in C versus Python. Python is then described as a dynamic, open source language designed for simplicity and productivity. Key features of Python like its interpreter-based nature, clear syntax, portability, large standard library, and suitability for many types of applications are outlined. The document demonstrates basic Python concepts like indentation, lack of variable typing, input/output, comments, and popular Python environments. It concludes by providing references to learn more about Python and announcing an upcoming PyCon conference.
This document provides an overview of a training session on Python and Django basics. Session 1 introduces Python, including its syntax, object orientation, dynamic typing, standard libraries, and extensibility. It also covers getting started with Django by creating projects and apps using django-admin.py and manage.py. The session explains Django's basic architecture, including urls.py, views.py, and templates. It concludes by discussing project structure and organizing static and media files. Upcoming Session 2 will cover models, the object-relational mapper (ORM), and working with databases in Django projects.
This document discusses combining Rust and Python to create a new "hip" programming language. It proposes two approaches: 1) Building Rust extensions for Python to improve performance of Python code. Rust could replace C and provide memory safety and better performance for Python extensions. 2) Building a Python interpreter using Rust (RustPython), which provides benefits like memory safety and borrowing rules from Rust. However, a Rust-based Python interpreter still has a long way to go before matching the performance and capabilities of CPython. In the end, the document acknowledges both Rust and Python have limitations and neither can fully "replace" the other.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...Tatiana Kojar
Skybuffer AI, built on the robust SAP Business Technology Platform (SAP BTP), is the latest and most advanced version of our AI development, reaffirming our commitment to delivering top-tier AI solutions. Skybuffer AI harnesses all the innovative capabilities of the SAP BTP in the AI domain, from Conversational AI to cutting-edge Generative AI and Retrieval-Augmented Generation (RAG). It also helps SAP customers safeguard their investments into SAP Conversational AI and ensure a seamless, one-click transition to SAP Business AI.
With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
Trusted Execution Environment for Decentralized Process MiningLucaBarbaro3
Presentation of the paper "Trusted Execution Environment for Decentralized Process Mining" given during the CAiSE 2024 Conference in Cyprus on June 7, 2024.
A Comprehensive Guide to DeFi Development Services in 2024Intelisync
DeFi represents a paradigm shift in the financial industry. Instead of relying on traditional, centralized institutions like banks, DeFi leverages blockchain technology to create a decentralized network of financial services. This means that financial transactions can occur directly between parties, without intermediaries, using smart contracts on platforms like Ethereum.
In 2024, we are witnessing an explosion of new DeFi projects and protocols, each pushing the boundaries of what’s possible in finance.
In summary, DeFi in 2024 is not just a trend; it’s a revolution that democratizes finance, enhances security and transparency, and fosters continuous innovation. As we proceed through this presentation, we'll explore the various components and services of DeFi in detail, shedding light on how they are transforming the financial landscape.
At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
A Python Tutorial
1. A Python Tutorial
Computer Science and
Engineering Association
NIT Calicut
Code Maestros
2. Contents
● Why Python?
● Interpreter Fun
● Live Demo
● Scripts
● Examples and QA
● Python in...
● References
● License and Sharing Info
3. Why Python?
● Popular
● Open Source
● Cross Platform
● Easy to learn
● Forces the programmer to write readable code
● General purpose - used almost everywhere from games
to robotics
4. Interpreter Fun
● Python interpreter - good for little experiments
● read-eval-print loop
● No need to declare variables
● Variables don't have types, but values do
k4rtik@PlatiniumLight ~ $ python
Python 2.7.2+ (default, Oct 4 2011, 20:06:09)
[GCC 4.6.1] on linux2
Type "help", "copyright", "credits" or
"license" for more information.
>>>
6. Scripts (like bash!)
#!/usr/bin/python
import sys
a = 123
def cat(filename):
"""Given filename, print its text contents."""
print filename, '======='
f = open(filename, 'r')
for line in f:
print line,
f.close()
7. Continues...
def main():
args = sys.argv[1:]
for filename in args:
if filename == 'voldemort'or filename == 'vader':
print 'this file is very worrying'
cat(filemane, 123, bad_variable)
else:
cat(filename)
print 'all done'
if __name__ == '__main__':
main()