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This #goodolddays box is #true - except things are MUCH BETTER NOW! But if you want to #learn #programming - well good luck. #Python #basic -erUr5W5beD1

This #goodolddays box is #true - except things are MUCH BETTER NOW! But if you want to #learn #programming - well good luck. #Python #basic -erUr5W5beD1

network

random2, 'Andrew Park - Data Science for Beginners_ 4 Books in 1_ Python Programming, Data Analysis, Machine Learning. A Complete Overview to Master The Art of Data Science From Scratch Using Python for Busines'

random2, 'Andrew Park - Data Science for Beginners_ 4 Books in 1_ Python Programming, Data Analysis, Machine Learning. A Complete Overview to Master The Art of Data Science From Scratch Using Python for Busines'

v, 29 pages : 25 cm

Learn how to create a picture display. This lecture will mainly concentrate on the idea of variables. The different types of variables includes integer, float, string and boolean. We will also take a look at the concept of global and local variables in python.

Advanced Perspective of Classes and Object-Oriented Programming in Python

Python

Python

Python programming guide for beginners to pro level

Python programming guide for beginners to pro level

PYTHON PROGRAMMING DVD ISO

PYTHON PROGRAMMING DVD ISO

PYTHON PROGRAMMING DVD ISO

PYTHON PROGRAMMING DVD ISO

xvooo. 477 p. : 25 cm

Lara Kattan https://www.pyohio.org/2019/presentations/116 Let's build up our knowledge of probabilistic programming and Bayesian inference! All you need to start is basic knowledge of linear regression; familiarity with running a model of any type in Python is helpful. By the end of this presentation, you'll know the following: - What probabilistic programming is and why it's necessary for Bayesian inference - What Bayesian inference is, how it's different from classical frequentist inference, and why it's becoming so relevant for applied data science in the real world - How to write your own Bayesian models in the Python library PyMC3, including metrics for judging how well the model is performing - How to go about learning more about the topic of Bayesian inference and how to bring it to your current data science job We'll meet our objectives by answering three questions: 1. What is probabilistic programming? * PP is the idea that we can use computer code to build probability distributions * Theory of the primitives in probabilistic programming and how we can build models out of distributions 2. What is Bayesian inference and why should I add it to my toolbox on top of classical ML models? * Classically, we had simulations, but they run in only one direction: get data input and move it according to assumptions of parameters and get a prediction * Bayesian inference adds another direction: use the data to go back and pick one of many possible parameters as the most likely to have created the data (posterior distributions) * Use Bayes' theorem to find the most likely values of the model parameters 3. What is PyMC3 and how can I start building and interpreting models using it? * **We'll work through actual examples of models using PyMC3, including hierarchical models** * Solving Bayes' theorem in practice requires taking integrals * If we don't want to do integrals by hand, we need to use numerical solution methods * From the package authors: "[PyMC3 is an ]open source probabilistic programming framework written in Python that uses Theano to compute gradients via automatic differentiation as well as compile probabilistic programs on-the-fly to C for increased speed" The intention is to get hands-on experience building PyMC3 models to demystify probabilistic programming / Bayesian inference for those more well versed in traditional ML, and, most importantly, to understand how these models can be relevant in our daily work as data scientists in business. If you can write a model in sklearn, you can make the leap to Bayesian inference with PyMC3, a user-friendly intro to probabilistic programming (PP) in Python. PP just means building models where the building blocks are probability distributions! And we can use PP to do Bayesian inference easily. Bayesian inference allows us to solve problems that aren't otherwise tractable with classical methods. === https://pyohio.org A FREE annual conference for anyone interested in Python in and around Ohio, the entire Midwest, maybe even the whole world. Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1 Sun Jul 28 13:15:00 2019 at Suzanne Scharer

Lara Kattan https://www.pyohio.org/2019/presentations/116 Let's build up our knowledge of probabilistic programming and Bayesian inference! All you need to start is basic knowledge of linear regression; familiarity with running a model of any type in Python is helpful. By the end of this presentation, you'll know the following: - What probabilistic programming is and why it's necessary for Bayesian inference - What Bayesian inference is, how it's different from classical frequentist inference, and why it's becoming so relevant for applied data science in the real world - How to write your own Bayesian models in the Python library PyMC3, including metrics for judging how well the model is performing - How to go about learning more about the topic of Bayesian inference and how to bring it to your current data science job We'll meet our objectives by answering three questions: 1. What is probabilistic programming? * PP is the idea that we can use computer code to build probability distributions * Theory of the primitives in probabilistic programming and how we can build models out of distributions 2. What is Bayesian inference and why should I add it to my toolbox on top of classical ML models? * Classically, we had simulations, but they run in only one direction: get data input and move it according to assumptions of parameters and get a prediction * Bayesian inference adds another direction: use the data to go back and pick one of many possible parameters as the most likely to have created the data (posterior distributions) * Use Bayes' theorem to find the most likely values of the model parameters 3. What is PyMC3 and how can I start building and interpreting models using it? * **We'll work through actual examples of models using PyMC3, including hierarchical models** * Solving Bayes' theorem in practice requires taking integrals * If we don't want to do integrals by hand, we need to use numerical solution methods * From the package authors: "[PyMC3 is an ]open source probabilistic programming framework written in Python that uses Theano to compute gradients via automatic differentiation as well as compile probabilistic programs on-the-fly to C for increased speed" The intention is to get hands-on experience building PyMC3 models to demystify probabilistic programming / Bayesian inference for those more well versed in traditional ML, and, most importantly, to understand how these models can be relevant in our daily work as data scientists in business. If you can write a model in sklearn, you can make the leap to Bayesian inference with PyMC3, a user-friendly intro to probabilistic programming (PP) in Python. PP just means building models where the building blocks are probability distributions! And we can use PP to do Bayesian inference easily. Bayesian inference allows us to solve problems that aren't otherwise tractable with classical methods. === https://pyohio.org A FREE annual conference for anyone interested in Python in and around Ohio, the entire Midwest, maybe even the whole world. Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1 Sun Jul 28 13:15:00 2019 at Suzanne Scharer

The Python DB-API 2.0 provides a direct interface to many popular database backends. It makes interaction with relational database very straight forward and allows tapping into the full set of features these databases provide. The talk will cover advanced database topics which are relevant in production environments such as locks, distributed transactions and transaction isolation. ---- The talk will give an in-depth discussion of advanced database programming topics based on the Python DB-API 2.0: locks and dead-locks, two-phase commits, transaction isolation, result set scrolling, schema introspection and handling multiple result sets. Talks slides are available on request.

I’m currently learning Python Programming to build a strong foundation in coding. This course is helping me understand how to write clean, efficient code and solve real-world problems using Python. I'm excited to apply these skills in web development, data analysis, and automation.

I’m currently learning Python Programming to build a strong foundation in coding. This course is helping me understand how to write clean, efficient code and solve real-world problems using Python. I'm excited to apply these skills in web development, data analysis, and automation.

I’m currently learning Python Programming to build a strong foundation in coding. This course is helping me understand how to write clean, efficient code and solve real-world problems using Python. I'm excited to apply these skills in web development, data analysis, and automation.

I’m currently learning Python Programming to build a strong foundation in coding. This course is helping me understand how to write clean, efficient code and solve real-world problems using Python. I'm excited to apply these skills in web development, data analysis, and automation.

Perma.cc archive of https://www.coursera.org/specializations/python-3-programming created on 2022-01-28 16:57:12.135711+00:00.

Perma.cc archive of https://www.coursera.org/specializations/python-3-programming created on 2022-01-28 16:57:12.135711+00:00.

Our hybrid programming journey with Python and C++: James Saunders's talk at PyConZA 2012

Python Object Oriented Programming OOP For Beginners

Python Object Oriented Programming OOP For Beginners

Welcome to the world of network programming with Python. Python is a full-featured object-oriented programming language with a standard library that includes everything needed to rapidly build powerful network applications. In addition, it has a multitude of third-party libraries and packages that extend Python to every sphere of network programming. Combined with the fun of using Python, with this book, we hope to get you started on your journey so that you master these tools and produce some great networking code. In this book, we are squarely targeting Python 3. Although Python 3 is still establishing itself as the successor to Python 2, version 3 is the future of the language, and we want to demonstrate that it is ready for network programming prime time. It offers many improvements over the previous version, many of which improve the network programming experience, with enhanced standard library modules and new additions. We hope you enjoy this introduction to network programming with Python. Dr. M. O. Faruque Sarker    Sam Washington

This open access book offers an initial introduction to programming for scientific and computational applications using the Python programming language. The presentation style is compact and example-based, making it suitable for students and researchers with little or no prior experience in programming. The book uses relevant examples from mathematics and the natural sciences to present programming as a practical toolbox that can quickly enable readers to write their own programs for data processing and mathematical modeling. These tools include file reading, plotting, simple text analysis, and using NumPy for numerical computations, which are fundamental building blocks of all programs in data science and computational science. At the same time, readers are introduced to the fundamental concepts of programming, including variables, functions, loops, classes, and object-oriented programming. Accordingly, the book provides a sound basis for further computer science and programming studies.

This open access book offers an initial introduction to programming for scientific and computational applications using the Python programming language. The presentation style is compact and example-based, making it suitable for students and researchers with little or no prior experience in programming. The book uses relevant examples from mathematics and the natural sciences to present programming as a practical toolbox that can quickly enable readers to write their own programs for data processing and mathematical modeling. These tools include file reading, plotting, simple text analysis, and using NumPy for numerical computations, which are fundamental building blocks of all programs in data science and computational science. At the same time, readers are introduced to the fundamental concepts of programming, including variables, functions, loops, classes, and object-oriented programming. Accordingly, the book provides a sound basis for further computer science and programming studies.

This open access book offers an initial introduction to programming for scientific and computational applications using the Python programming language. The presentation style is compact and example-based, making it suitable for students and researchers with little or no prior experience in programming. The book uses relevant examples from mathematics and the natural sciences to present programming as a practical toolbox that can quickly enable readers to write their own programs for data processing and mathematical modeling. These tools include file reading, plotting, simple text analysis, and using NumPy for numerical computations, which are fundamental building blocks of all programs in data science and computational science. At the same time, readers are introduced to the fundamental concepts of programming, including variables, functions, loops, classes, and object-oriented programming. Accordingly, the book provides a sound basis for further computer science and programming studies.

If you want a basic understanding of computer vision’s underlying theory and algorithms, this hands-on introduction is the ideal place to start. You’ll learn techniques for object recognition, 3D reconstruction, stereo imaging, augmented reality, and other computer vision applications as you follow clear examples written in Python. Programming Computer Vision with Python  explains computer vision in broad terms that won’t bog you down in theory. You get complete code samples with explanations on how to reproduce and build upon each example, along with exercises to help you apply what you’ve learned. This book is ideal for students, researchers, and enthusiasts with basic programming and standard mathematical skills. Learn techniques used in robot navigation, medical image analysis, and other computer vision applications Work with image mappings and transforms, such as texture warping and panorama creation Compute 3D reconstructions from several images of the same scene Organize images based on similarity or content, using clustering methods Build efficient image retrieval techniques to search for images based on visual content Use algorithms to classify image content and recognize objects Access the popular OpenCV library through a Python interface

Python - Oriented Programming - Fastener Joint Design

GUI Tutorial 2-Python Tkinter -Quadratic Equation - Computer Programming Tutor

Kenneth and Wynn caught up with Glyph Lefkowitz from Twisted to talk about the project and evented programming in Python.

[ Reading] After Work Introduction To Python Programming

GUI Tutorial 0-Python Tkinter -Tutorial for Beginners-Computer Programming Tutor

random2, 'Andrew Park - Data Science for Beginners_ 4 Books in 1_ Python Programming, Data Analysis, Machine Learning. A Complete Overview to Master The Art of Data Science From Scratch Using Python for Busines'

Python Programming Collection PDF Ebooks All You Need

This video will teach you everything you need as a beginner to write Python programs using PyCharm on any Linux distro and Windows 11. 00:00 Intro 00:18 How to open PyCharm on Linux 00:37 How to open PyCharm on Windows 11 00:14 How to exit project in PyCharm 01:20 How to open projects in PyCharm 01:35 How to create new projects in PyCharm 03:10 How to maximize the PyCharm window 03:19 How to change the PyCharm theme 03:46 Introduction to PyCharm project tool window 03:54 How to hide items in the folder in PyCharm project tool window 04:00 How to view items in the folder in PyCharm project tool window 04:05 How to create a new folder using the PyCharm project tool window 04:37 How to create a new Python script using the PyCharm project tool window 05:24 How to close Python Scripts in the PyCharm editor panel 05:31 How to open Python Scripts in the PyCharm editor panel using PyCharn project tool window 05:39 PyCharm project conclusion 05:51 How to minimize the “project tool window” 06:00 How to make the “Project tool window” show up 06:14 Introduction to PyCharm editor panel 06:20 How to edit Python script using PyCharm editor panel 07:00 How to use the "cd" command to move around the file path in PyCharm terminal 07:49 How to use the "ls" command to see a list of all the folders and files contained inside your current folder in the PyCharm terminal 08:00 Example of using the "cd" command in the PyCharm terminal 08:34 How to create folders using the PyCharm terminal using the "mkdir" command 09:31 How to create Python files using the PyCharm terminal using the "touch" command 10:31 How to run Python script using the PyCharm terminal 12:07 How to hide and unhide the PyCharn terminal 12:27 PyCharm terminal conclusion 12:31 How you can run a Python script in PyCharm without using the terminal 12:59 How to close the PyCharm app 13:11 What to do if you need help or want to improve using PyCharm. 13:41 Outro

GUI Tutorial 3-Python Tkinter -Area Of Triangle-Computer Programming Tutor

random2, 'Andrew Park - Data Science for Beginners_ 4 Books in 1_ Python Programming, Data Analysis, Machine Learning. A Complete Overview to Master The Art of Data Science From Scratch Using Python for Busines'

Mathematics; Computer mathematics; Numerical analysis; Computer software; Numerical analysis

Numerical simulations; programming; Python

Numerical simulations; programming; Python

Nicolas Lara - Python and Async programming [EuroPython 2016] [18 July 2016 / 2016-07-18] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/python-and-async-programming) How does the experienced python programmer fair when faced with python's "new" way of doing async programming for the first time? This talk details the different ways python provides for attacking the problem of asynchronous programming and focuses on the best practices for the future (as of python 3.4 and 3.5) ----- How does the experienced python programmer fair when faced with python's "new" way of doing async programing in for the first time? Do we all know how and when to use Futures, yield from, asyncio, coroutines, the async and await keywords, eventloops, and others? A lot has changed in recent versions of Python when it comes to async programming, concurrency, and parallelism. We still have very different ways of approaching the problem in each version, but they are finally (as of python 3.4/3.5) converging to a standard. This talk explores, from the perspective of an experienced python programmer with little to no experience in async programming, what the "one obvious way" to do async programming in Python is supposed to be. It does so but analysing examples of different categories of async problems we may want to solve and what the correct way to solve them with the latest versions of Python would be (along with the trade offs of different approaches). The examples include generic CPU-bound problems, IO-bound problems, and "both-bound" problems; along with common tasks as building a simple server, scraping, deferring a web response, and traversing graphs. When useful, I compare the solutions with the approach we would take in languages that have been design for- and are known to be good at async programming like Javascript and Go.

you will learn the Tkinter library, how to draw buttons, windows, textfields, etc, a good starting point to learn GUI programming. Watching previous lectures are recommended to understand this lecture. Have you ever wanted to know how your application is programmed on the computer. This lecture will show you how easy it is to program. Aimed at beginner programmers or people that has no programming experience. For you to watch this class, you must have python installed from python.org . Lecture 2 will show you how to install and run python. This lecture assumes that you already have python. The python course series is designed to be short, fun, and concise. They are 10 minutes each with fun examples and easy instruction. If you want to learn programming this is the way to go.

Hi, & Welcome to this new how to series of student video tutorial. My name is Bijay, and i’ll be teaching you complete series of Python Programming for Hackers. This is first part, Part 1 – Introduction to course outline & setup process.