Best Web Crawling Method and Tutorial
Views: 16474 Umer Javed
This course will give you a full introduction into all of the core concepts in python. Follow along with the videos and you'll be a python programmer in no time! ⭐️ Contents ⭐ ⌨️ (0:00) Introduction ⌨️ (1:45) Installing Python & PyCharm ⌨️ (6:40) Setup & Hello World ⌨️ (10:23) Drawing a Shape ⌨️ (15:06) Variables & Data Types ⌨️ (27:03) Working With Strings ⌨️ (38:18) Working With Numbers ⌨️ (48:26) Getting Input From Users ⌨️ (52:37) Building a Basic Calculator ⌨️ (58:27) Mad Libs Game ⌨️ (1:03:10) Lists ⌨️ (1:10:44) List Functions ⌨️ (1:18:57) Tuples ⌨️ (1:24:15) Functions ⌨️ (1:34:11) Return Statement ⌨️ (1:40:06) If Statements ⌨️ (1:54:07) If Statements & Comparisons ⌨️ (2:00:37) Building a better Calculator ⌨️ (2:07:17) Dictionaries ⌨️ (2:14:13) While Loop ⌨️ (2:20:21) Building a Guessing Game ⌨️ (2:32:44) For Loops ⌨️ (2:41:20) Exponent Function ⌨️ (2:47:13) 2D Lists & Nested Loops ⌨️ (2:52:41) Building a Translator ⌨️ (3:00:18) Comments ⌨️ (3:04:17) Try / Except ⌨️ (3:12:41) Reading Files ⌨️ (3:21:26) Writing to Files ⌨️ (3:28:13) Modules & Pip ⌨️ (3:43:56) Classes & Objects ⌨️ (3:57:37) Building a Multiple Choice Quiz ⌨️ (4:08:28) Object Functions ⌨️ (4:12:37) Inheritance ⌨️ (4:20:43) Python Interpreter Course developed by Mike Dane. Check out his YouTube channel for more great programming courses: https://www.youtube.com/channel/UCvmINlrza7JHB1zkIOuXEbw 🐦Follow Mike on Twitter - https://twitter.com/mike_dane 🔗If you liked this video, Mike accepts donations on his website: https://www.mikedane.com/contribute/ ⭐️Other full courses by Mike Dane on our channel ⭐️ 💻C: https://youtu.be/KJgsSFOSQv0 💻C++: https://youtu.be/vLnPwxZdW4Y 💻SQL: https://youtu.be/HXV3zeQKqGY 💻Ruby: https://youtu.be/t_ispmWmdjY 💻PHP: https://youtu.be/OK_JCtrrv-c 💻C#: https://youtu.be/GhQdlIFylQ8 -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://medium.freecodecamp.org And subscribe for new videos on technology every day: https://youtube.com/subscription_center?add_user=freecodecamp
Views: 5062824 freeCodeCamp.org
In this tutorial, we shall demonstrate you how to extract texts from any image in python. So we shall write a program in python using the module pytesseract that will extract text from any image like .jpg, .jpeg, .png etc. Please subscribe to my youtube channel for such tutorials Watch the same tutorial on how to extract text from an image in Linux below: https://youtu.be/gLUQ8uaaw8A Please watch the split a file by line number here: https://youtu.be/ADRmbu3puCg Split utility in Linux/Unix : to break huge file into small pieces https://www.youtube.com/watch?v=ADRmbu3puCg How to keep sessions alive in terminal/putty infinitely in linux/unix : Useful tips https://www.youtube.com/watch?v=ARIgHdpxaU8 Random value generator and shuffling in python https://www.youtube.com/watch?v=AKwnQQ8TBBM Intro to class in python https://www.youtube.com/watch?v=E6kKZXHS5hM Lists, tuples, dictionary in python https://www.youtube.com/watch?v=Axea1CSewzc Python basic tutorial for beginners https://www.youtube.com/watch?v=_JyjbZc0euY Python basics tutorial for beginners part 2 -variables in python https://www.youtube.com/watch?v=ZlsptvP69NU Vi editor basic to advance part 1 https://www.youtube.com/watch?v=vqxQx-NNyFM Vi editor basic to advance part 2 https://www.youtube.com/watch?v=OWKp2DLaFyY Keyboard remapping in linux, switching keys as per your own choice https://www.youtube.com/watch?v=kJz7uKDyZjs How to install/open an on sceen keyboard in Linux/Unix system https://www.youtube.com/watch?v=d71i9SZX6ck Python IDE for windows , linux and mac OS https://www.youtube.com/watch?v=-tG54yoDs68 How to record screen or sessions in Linux/Unix https://www.youtube.com/watch?v=cx59c15-c8s How to download and install PAGE GUI builder for python https://www.youtube.com/watch?v=dim725Px2hM Create a basic Login page in python using GUI builder PAGE https://www.youtube.com/watch?v=oCAWWUhwEUQ Working with RadioButton in python in PAGE builder https://www.youtube.com/watch?v=YJbQvpzJDr4 Basic program on Multithreading in python using thread module https://www.youtube.com/watch?v=RGm3989ekAc
Views: 24305 LinuxUnixAix
Get 80% off the full course from this link: https://www.udemy.com/automate/?couponCode=FOR_LIKE_10_BUCKS Support me on Patreon: https://www.patreon.com/AlSweigart Buy the print book here: https://www.amazon.com/gp/product/1593275994/ref=as_li_qf_sp_asin_il_tl?ie=UTF8&tag=playwithpyth-20&camp=1789&creative=9325&linkCode=as2&creativeASIN=1593275994&linkId=8a8e0ae7d1b277b2352cb8006ba5de09 Lesson 8 of the online Python programming course for complete beginners. This course follows the "Automate the Boring Stuff with Python" book by Al Sweigart, which can be read online at http://automatetheboringstuff.com Lesson 8 covers import Statements, sys.exit(), the pyperclip Module. These concepts are explained in more detail at https://automatetheboringstuff.com/chapter2/
Views: 142678 Al Sweigart
In computing, stop words are words which are filtered out prior to, or after, processing of natural language data (text). There is not one definite list of stop words which all tools use and such a filter is not always used. Some tools specifically avoid removing them to support phrase search. Any group of words can be chosen as the stop words for a given purpose. For some search machines, these are some of the most common, short function words, such as the, is, at, which, and on. In this case, stop words can cause problems when searching for phrases that include them, particularly in names such as 'The Who', 'The The', or 'Take That'. Other search engines remove some of the most common words—including lexical words, such as "want"—from a query in order to improve performance. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 589 Audiopedia
See how PoolParty's taxonomy management methodology (https://www.poolparty.biz) is now supported even more efficiently by PoolParty's latest release. We demonstrate how PoolParty 5.2 makes use of deep text mining including corpus analysis and co-occurrence analysis. We show an example based on UNESCO world heritage sites and demonstrate how automatic classification can be extended step-by-step. An immediate feedback is given by PoolParty's faceted GraphSearch. Initial taxonomies can be built by using PoolParty's linked data harvester to fetch data from DBpedia.
Views: 959 PoolParty Semantic Suite
In this Python 3 programming tutorial, we cover how to read data in from a CSV spreadsheet file. CSV, literally standing for comma separated variable, is just a file that has data that is separated by some sort of delimiter, it does not have to be a comma. Luckily for us, Python 3 has a built in module for reading and writing from and to CSV files! Sample code for this basics series: http://pythonprogramming.net/beginner-python-programming-tutorials/ Python 3 Programming tutorial Playlist: http://www.youtube.com/watch?v=oVp1vrfL_w4&feature=share&list=PLQVvvaa0QuDe8XSftW-RAxdo6OmaeL85M http://seaofbtc.com http://sentdex.com http://hkinsley.com https://twitter.com/sentdex Bitcoin donations: 1GV7srgR4NJx4vrk7avCmmVQQrqmv87ty6
Views: 223818 sentdex
Welcome to a Python for Finance tutorial series. In this series, we're going to run through the basics of importing financial (stock) data into Python using the Pandas framework. From here, we'll manipulate the data and attempt to come up with some sort of system for investing in companies, apply some machine learning, even some deep learning, and then learn how to back-test a strategy. I assume you know the fundamentals of Python. If you're not sure if that's you, click the fundamentals link, look at some of the topics in the series, and make a judgement call. If at any point you are stuck in this series or confused on a topic or concept, feel free to ask for help and I will do my best to help. https://pythonprogramming.net https://twitter.com/sentdex https://www.facebook.com/pythonprogramming.net/ https://plus.google.com/+sentdex
Views: 294345 sentdex
I'll show you how you can turn an article into a one-sentence summary in Python with the Keras machine learning library. We'll go over word embeddings, encoder-decoder architecture, and the role of attention in learning theory. Code for this video (Challenge included): https://github.com/llSourcell/How_to_make_a_text_summarizer Jie's Winning Code: https://github.com/jiexunsee/rudimentary-ai-composer More Learning resources: https://www.quora.com/Has-Deep-Learning-been-applied-to-automatic-text-summarization-successfully https://research.googleblog.com/2016/08/text-summarization-with-tensorflow.html https://en.wikipedia.org/wiki/Automatic_summarization http://deeplearning.net/tutorial/rnnslu.html http://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/ Please subscribe! And like. And comment. That's what keeps me going. Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 154827 Siraj Raval
This is a tutorial concerning how to sort CSV files and lists easily within python by column. The logic possibly by programming plus the simplicity of being able to sort columns makes python a superb choice for managing CSV documents and lists that are delimited by something. Sentdex.com Facebook.com/sentdex Twitter.com/sentdex
Views: 47734 sentdex
Dans ce 9ème tutoriel pour apprendre à programmer en Python, découvrons comment manipuler les fichiers. Les vidéos de la série : - #1 : Les entrées sorties variables https://www.youtube.com/watch?v=PVB8Qn5bjqY - #2 : Les conditions et booléens https://www.youtube.com/watch?v=tWXSI0qN_To - #3 : Les listes https://www.youtube.com/watch?v=Kwxdlu2JB9w - #4 : Les boucles https://www.youtube.com/watch?v=rrWQ7s2hTFg - #5 : Deviner le nombre (exercice) https://www.youtube.com/watch?v=e9JJsfGLk2w - #6 : Les fonctions https://www.youtube.com/watch?v=WRm6_yLtseQ - #7 : Les modules https://www.youtube.com/watch?v=qqZEpqHM7UQ - #8 : Les structures de données (tuples, dictionnaires...) https://www.youtube.com/watch?v=5ZsPMfnlk5A - #9 : Les fichiers https://www.youtube.com/watch?v=mq1KqzmbEMs - #10 : Analyse démographique (exercice) https://www.youtube.com/watch?v=CtLThUDOzhA Quelques liens : - mon site internet : http://www.lucaswillems.com - mon twitter : http://twitter.com/lcswillems
Views: 26597 Lucas Willems
First video of our latest course by Daniel Chen: Cleaning Data in Python. Like and comment if you enjoyed the video! A vital component of data science involves acquiring raw data and getting it into a form ready for analysis. In fact, it is commonly said that data scientists spend 80% of their time cleaning and manipulating data, and only 20% of their time actually analyzing it. This course will equip you with all the skills you need to clean your data in Python, from learning how to diagnose your data for problems to dealing with missing values and outliers. At the end of the course, you'll apply all of the techniques you've learned to a case study in which you'll clean a real-world Gapminder dataset! So you've just got a brand new dataset and are itching to start exploring it. But where do you begin, and how can you be sure your dataset is clean? This chapter will introduce you to the world of data cleaning in Python! You'll learn how to explore your data with an eye for diagnosing issues such as outliers, missing values, and duplicate rows. Try the first chapter for free: https://www.datacamp.com/courses/cleaning-data-in-python
Views: 14997 DataCamp
This is the second in a three-part video series that introduces IBM ILOG CPLEX Optimization Studio. In this video, we look at how we can quickly get started with creating an optimization model in the Optimization Programming Language (OPL) and Python. You can explore IBM ILOG CPLEX Optimization Studio in greater detail by visiting: https://ibm.co/2JFDNTx CPLEX Fundamentals Tutorial: https://ibm.co/2Jwd14a Optimization models in Python: https://ibm.co/2JtuKsK Please note that the transcript for this video has been translated into French, German, Spanish, and Simplified Chinese. To view the subtitles, click on the 'Settings' icon on the bottom right of the video, click on 'Subtitles,' and then select what language you want to view the transcript in
Views: 6640 IBM Analytics
in this video we will understand Python Data types in Hindi. what is String in python what is integer in python what is float in python what is list in python what is tuple in python what is Dictionary in python Copyright © 2014 by Rajiv Sharma ([email protected]) All Rights Reserved. VFXPipeline YouTube Channel and its content is copyright of Rajiv Sharma. Any redistribution or reproduction of part or all of the contents in any form is prohibited other than the following: 1.you can not remove starting 3 second vfxpipeline intro 2.you can not re-upload vfxpipeline channel videos on YouTube or any other website. 3.you can share the links of vfxpipeline channel videos. 4.you can download and share with others for Free. 5.All Free Content : you can not sell vfxpipeline channel videos
Views: 16432 VFX Pipeline
Explore the full course on Udemy (special discount included in the link): https://www.udemy.com/draft/1337374/?couponCode=PYYOUTUBENARRATIVE This course is designed to take you from a complete beginner in programming all the way to becoming an effective programmer that can use Python to solve real tasks! I am Jose Portilla and I am the most popular Python instructor on the Udemy platform. I have taught Python programming at Fortune 500 companies and I am very excited to bring the same quality of material to Udemy! Python is used by some of the world largest companies to accomplish all kinds of tasks. This course is also completely different than any other course on Udemy, it incorporates a narrative story that helps engage students and also provides context to the different tasks you have to accomplish. We utilize project based learning to effectively teach Python and give you the skills to put Python on your resume. We have numerous projects and tasks for you to practice what you are learning. In addition to this we have Question and Answer forums where Teaching Assistants and myself are present to help answer any questions you may have, we also have a chat channel where you can talk to other students to team up on your own projects! We will cover a lot of topics in this course! Including: Basic Python Data Types such as numbers, variables, lists, dictionaries, tuples, sets, and more. Key Control Flow - This is the logic that helps run your code, such as if, elif, and else statements. Loops - We'll show you how to become an expert user of for loops and while loops so you can effectively program. Functions - You will learn how to create clean, reusable functions that help automate tasks that you repeat. Object Oriented Programming (OOP) - We will explain OOP in a clear and steady way, helping you master one of Python's most powerful features. Web Scraping - Learn to use the BeautifulSoup and Requests libraries to perform web scraping. CSV Files - You'll be able to use Python's built in csv library to work with csv data with Python. PDF Files - Learn about the PyPDF2 library that allows you to read PDF files pro grammatically. Zip Files - See how Python can zip files and extract information from already compressed zip files. OS Module - Discover how to perform operating system level commands with Python's os module. Images - You will learn how to edit and resize images with Python. Decryption and Encryption - See how to use the cryptography library with Python to encode and decode encrypted messages. Geographical Mapping - We'll show you how to use Python in conjunction with the Google Map's API to plot information on a map! and so much more!
Views: 503 Udemy Tech
Learn more about credit risk modeling in R: https://www.datacamp.com/courses/introduction-to-credit-risk-modeling-in-r We have seen several techniques for preprocessing the data. When the data is fully preprocessed, you can go ahead and start your analysis. You can run the model on the entire data set, and use the same data set for evaluating the result, but this will most likely lead to a result that is too optimistic. One alternative is to split the data into two pieces. The first part of the data, the so-called training set, can be used for building the model and the second part of the data, the test set, can be used to test the results. One common way of doing this is to use two-thirds of the data for a training set and one-third of the data for the test set. Of course there can be a lot of variation in the performance estimate depending which two-thirds of the data you select for the training set. One way to reduce this variation is by using cross validation. For the two-thirds training set and one-third test set example, a cross validation variant would look like this. The data would be split in three equal parts, and each time, two of these parts would act as a training set, and one part would act as a test set. Of course, we could use as many parts as we want, but we would have to run the model many times if using many parts. This may become computationally heavy. In this course, we will just use one training set and one test set containing two-thirds versus one-third of the data, respectively. Imagine we have just run a model, and now we apply the model to our test set to see how good the results are. Evaluating the model for credit risk means comparing the observed outcomes of default versus non-default--stored in the loan_status variable of the test set--with the predicted outcomes according to the model. If we are dealing with a large number of predictions, a popular method for summarizing the results uses something called a confusion matrix. Here, we use just 14 values to demonstrate the concept. A confusion matrix is a contingency table of correct and incorrect classifications. Correct classifications are on the diagonal of the confusion matrix. We see, for example, that 8 non-defaulters were correctly classified as non-default, and 3 defaulters were correctly classified as defaulters. However, we see that 2 non-defaulters where wrongly classified as defaulters, and 1 defaulter was wrongly classified as a non-defaulter. The items on the diagonals are also called the true positives and true negatives. The off-diagonals are called the false positives versus the false negatives. Several measures can be derived from the confusion matrix. We will discuss the classification accuracy, the sensitivity and the specificity. The classification accuracy is the percentage of correctly classified instances, which is equal to 78.57% in this example. The sensitivity is the percentage of good customers that are classified correctly, or 75% in this example. The specificity is the percentage of bad costomers that are classified correctly, or 0.80 in this example. Let's practice splitting the data and constructing confusion matrices.
Views: 13849 DataCamp
( Python Training : https://www.edureka.co/python ) This Edureka Python tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) gives an introduction to Machine Learning and how to implement machine learning algorithms in Python. Below are the topics covered in this tutorial: 1. Why Machine Learning? 2. What is Machine Learning? 3. Types of Machine Learning 4. Supervised Learning 5. KNN algorithm 6. Unsupervised Learning 7. K-means Clustering Algorithm Check out our playlist for more videos: https://goo.gl/Na1p9G Subscribe to our channel to get video updates. Hit the subscribe button above. #Python #PythonTutorial #PythonMachineLearning #PythonTraining How it Works? 1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course Edureka's Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you: 1. Master the Basic and Advanced Concepts of Python 2. Understand Python Scripts on UNIX/Windows, Python Editors and IDEs 3. Master the Concepts of Sequences and File operations 4. Learn how to use and create functions, sorting different elements, Lambda function, error handling techniques and Regular expressions ans using modules in Python 5. Gain expertise in machine learning using Python and build a Real Life Machine Learning application 6. Understand the supervised and unsupervised learning and concepts of Scikit-Learn 7. Master the concepts of MapReduce in Hadoop 8. Learn to write Complex MapReduce programs 9. Understand what is PIG and HIVE, Streaming feature in Hadoop, MapReduce job running with Python 10. Implementing a PIG UDF in Python, Writing a HIVE UDF in Python, Pydoop and/Or MRjob Basics 11. Master the concepts of Web scraping in Python 12. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience - - - - - - - - - - - - - - - - - - - Why learn Python? Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license. Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Review Sairaam Varadarajan, Data Evangelist at Medtronic, Tempe, Arizona: "I took Big Data and Hadoop / Python course and I am planning to take Apache Mahout thus becoming the "customer of Edureka!". Instructors are knowledge... able and interactive in teaching. The sessions are well structured with a proper content in helping us to dive into Big Data / Python. Most of the online courses are free, edureka charges a minimal amount. Its acceptable for their hard-work in tailoring - All new advanced courses and its specific usage in industry. I am confident that, no other website which have tailored the courses like Edureka. It will help for an immediate take-off in Data Science and Hadoop working."
Views: 148737 edureka!
David Beazley What's more fun than learning Python? Learning Python by hacking on public data! In this tutorial, you'll learn Python basics by reading files, scraping the web, building data structures, and analyzing real world data. By the end, you w
Views: 773570 Next Day Video
Learn how to read out data from an Excel document using the xlrd Python module. The xlsx and xls file formats are supported. xlrd docs: http://www.lexicon.net/sjmachin/xlrd.html Type Numbers: 0 - XL_CELL_EMPTY 1 - XL_CELL_TEXT 2 - XL_CELL_NUMBER 3 - XL_CELL_DATE 4 - XL_CELL_BOOLEAN 5 - XL_CELL_ERROR 6 - XL_CELL_BLANK
Views: 194494 triforcelink
PyData London 2016 Deep Boltzmann machines (DBMs) are exciting for a variety of reasons, principal among which is the fact that they are able to learn probabilistic representations of data in an entirely unsupervised manner. This allows DBMs to leverage large quantities of unlabelled data which are often available. The resulting representations can then be fine-tuned using limited labelled data or studied to obtain a more comprehensive understanding of the data at hand. This talk will begin by providing a high level description of DBMs and the training algorithms involved in learning such models. A topic modelling example will be used as a motivating example to discuss practical aspects of fitting DBMs and potential pitfalls. The entire code for this project is written in python using only standard libraries e.g., bumpy. Slides available here: https://github.com/piomonti/DeepTextMining/blob/master/PyData%20London%20Slides.pdf GitHub: https://github.com/piomonti/DeepTextMining
Views: 1657 PyData
by Associate Professor Ph.D. Pavlos Kollias, McGill University, Canada. Lecture 1 of the 2013 "Summer School on Remote Sensing of Clouds and Precipitation", at the Meteorological Institute of the University of Bonn. July 15-19, 2013. Filmed and produced by Uni-Bonn.TV Copyright by Universität Bonn
Views: 268 uni-bonn.tv
Beşinci python dersimizde Requests ve BeautifulSoup paketlerini kullanıyoruz. Hazırladığımı kodlar hem python 2, hem de python 3 serisinde aynen çalışmaktadır. Videoda işlenen kaynak kodlarına: http://gurmezin.com/python-requests-ve-beautifulsoup-paketleri/ adresinden ulaşabilirsiniz.
Views: 1112 Ahmet Aksoy
More videos like this online at http://www.theurbanpenguin.com We now have some more great fun and see how much we can use the shell for; creating reports easily from the command line against CSV files. The script should be quite easy to read now as we use a while loop to read in the CSV file. We change the file delimiter to be the comma and then we have the line that we read in broken up into the schema elements we need. A report then is easy with colours and search ability. This is very usable
Views: 50059 theurbanpenguin
Day 2 of the PyCascades Live stream. Full schedule of events available here: https://2019.pycascades.com/schedule/ Note: Individual releases of all the talks will come in the next few weeks. Our amazing videographer is already working on it. Please keep in mind that these videos are copyrighted and releasing and editing under your own account is not permitted.
Views: 1065 PyCascades
In this tutorial, I am going to explain how to fix most common error that every programmer face i.e Warning: mysqli_connect(): (HY000/1045): Access denied for user 'root'@'localhost' (using password: YES) in PHP. If you need help hire me at: http://www.corephpdeveloper.com
Views: 6496 360 Degree Tutorials
This is a spoken word version of the article Merkle Tree. Listen to this article (audio help) Duration: 04:45 Created by: slashdottir Date recorded: 2013-09-17 Corresponding article version: Click here to see the article as it was read Accent: Californian English Refer to: List of spoken articles Wikiproject Spoken Wikipedia Source: https://commons.wikimedia.org/wiki/File:En-Merkle_Tree.ogg License: CC-BY-SA 3.0 Picture: By Azaghal (Own work) [CC0], via Wikimedia Commons https://commons.wikimedia.org/wiki/File%3AHash_Tree.svg
Views: 15246 Spoken Wikipedia
August 2007 Meeting of the Bay Area Python Interest Group; Python Newbies night, featuring the second half of Alex Martelli's talk on "Python for Programmers."
Views: 10993 Google
Students who want to do final projects involving music have a wealth of free, open-source resources available to them but may not know where to look. This seminar will serve as a quick introduction to libraries and programs for several common tasks, including sound processing and analysis, MIDI synthesis, and music-score typesetting. The seminar will focus especially on Euterpea, a library in the Haskell programming language for algorithmic composition and MIDI synthesis; and on Lilypond, a LaTeX-like declarative language. No prior knowledge of Haskell is assumed; elementary concepts will be covered as necessary.
Views: 9049 CS50