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Report of PyCon 2011 sessions.
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aquibhussain torgal
This presentation discusses some of the work I did to port parts of the Twisted library to Python 3.
The Onward Journey: Porting Twisted to Python 3
The Onward Journey: Porting Twisted to Python 3
Craig Rodrigues
Presentation for the Transformative Code Pile 1 Programming Meetup on Aug 3, 2017 on expanding the Copycat Project into an AI Genetic Internet of Reactive Services
From Copycat Codelets to an AI Market Internet Protocol
From Copycat Codelets to an AI Market Internet Protocol
Stefan Ianta
Delivered by Ben Lerner at the 2016 New York R Conference on April 8th and 9th at Work-Bench.
High-Performance Python
High-Performance Python
Work-Bench
Functional coverages
Functional coverages
Gennadii Donchyts
Python 101 for the .NET Developer, to be delivered on Saturday, July 31, 2010 at PyOhio 2010
Python 101 for the .NET Developer
Python 101 for the .NET Developer
Sarah Dutkiewicz
SEVERAL TOPICS IN WORDNET PROJECTS SUPPORTIVE LANGUAGES IN WORDNET PROMINENT ALGORITHMS IN WORDNET
Wordnet Projects
Wordnet Projects
Phdtopiccom
Why use Python?
Why Python?
Why Python?
Adam Pah
Recomendados
Resume
Resume
aquibhussain torgal
This presentation discusses some of the work I did to port parts of the Twisted library to Python 3.
The Onward Journey: Porting Twisted to Python 3
The Onward Journey: Porting Twisted to Python 3
Craig Rodrigues
Presentation for the Transformative Code Pile 1 Programming Meetup on Aug 3, 2017 on expanding the Copycat Project into an AI Genetic Internet of Reactive Services
From Copycat Codelets to an AI Market Internet Protocol
From Copycat Codelets to an AI Market Internet Protocol
Stefan Ianta
Delivered by Ben Lerner at the 2016 New York R Conference on April 8th and 9th at Work-Bench.
High-Performance Python
High-Performance Python
Work-Bench
Functional coverages
Functional coverages
Gennadii Donchyts
Python 101 for the .NET Developer, to be delivered on Saturday, July 31, 2010 at PyOhio 2010
Python 101 for the .NET Developer
Python 101 for the .NET Developer
Sarah Dutkiewicz
SEVERAL TOPICS IN WORDNET PROJECTS SUPPORTIVE LANGUAGES IN WORDNET PROMINENT ALGORITHMS IN WORDNET
Wordnet Projects
Wordnet Projects
Phdtopiccom
Why use Python?
Why Python?
Why Python?
Adam Pah
Python is a widely used general-purpose, high-level programming language.Its design philosophy emphasizes code readability, and its syntax allows programmers to express concepts in fewer lines of code than would be possible in languages such as C.The language provides constructs intended to enable clear programs on both a small and large scale.Python supports multiple programming paradigms, including object-oriented, imperative and functional programming or procedural styles. It features a dynamic type system and automatic memory management and has a large and comprehensive standard library.
Python Online From EasyLearning Guru
Python Online From EasyLearning Guru
KCC Software Ltd. & Easylearning.guru
Get to know the usage of Python once you graduate from college. Also when and why you must avoid Python.
The Python outside of your textbook
The Python outside of your textbook
Aniket Prabhu
Girish one year
Girish one year
girish bb
Developers often wonder how to implement a certain functionality (e.g., how to parse XML files) using APIs. Obtaining an API usage sequence based on an API-related natural language query is very helpful in this regard. Given a query, existing approaches utilize information retrieval models to search for matching API sequences. These approaches treat queries and APIs as bags-of-words and lack a deep understanding of the semantics of the query. We propose DeepAPI, a deep learning based approach to generate API usage sequences for a given natural language query. Instead of a bag-of-words assumption, it learns the sequence of words in a query and the sequence of associated APIs. DeepAPI adapts a neural language model named RNN Encoder-Decoder. It encodes a word sequence (user query) into a fixed-length context vector, and generates an API sequence based on the context vector. We also augment the RNN Encoder-Decoder by considering the importance of individual APIs. We empirically evaluate our approach with more than 7 million annotated code snippets collected from GitHub. The results show that our approach generates largely accurate API sequences and outperforms the related approaches.
Deep API Learning (FSE 2016)
Deep API Learning (FSE 2016)
Sung Kim
Middleware fourth unit
Middleware fourth unit
selva kumar
Summer Research Project. Final Presentation 2013
Summer Research Project. Final Presentation 2013
Ojaswa Anand
Benefits of Extensions
Benefits of Extensions
Alexandro Colorado
Basic Presentation On python and it's Keywords..
Presentation on python
Presentation on python
william john
This presentation tells about Python and R, their differences and their interconnection.
Python and r in data science
Python and r in data science
Ravi Ranjan Prasad Karn
How to integrate python into a scala stack
How to integrate python into a scala stack
Fliptop
brf to mathml
brf to mathml
Adarsh Burma
How we can make a more cleaner testable code by applying functional programming ideas in Python
Functional programming ideas in python
Functional programming ideas in python
Manish Tomar
python tutorial from beginner
Python indroduction
Python indroduction
FEG
A presentation by Morteza Zakeri Iran University of Science and Technology Fall 2016
An Introduction to ANTLR
An Introduction to ANTLR
Morteza Zakeri
[PyCon 2014 APAC] How to integrate python into a scala stack to build realtim...
[PyCon 2014 APAC] How to integrate python into a scala stack to build realtim...
Jerry Chou
ZIB use Xeon Phi to achieve their Connected Compenent Labeling strategy #ISC13 #HPC
Connected Component Labeling on Intel Xeon Phi Coprocessors – Parallelization...
Connected Component Labeling on Intel Xeon Phi Coprocessors – Parallelization...
Intel IT Center
Slides for a talk given by Fernando Pérez at PyData Silicon Valley 2013
IPython: A Modern Vision of Interactive Computing (PyData SV 2013)
IPython: A Modern Vision of Interactive Computing (PyData SV 2013)
PyData
Python programming Launguage
Python presentation
Python presentation
gaganapponix
This is the Introduction to Python for Beginners
Introduction to python for Beginners
Introduction to python for Beginners
Sujith Kumar
OpenMI
DSD-INT 2014 - OpenMI symposium - OpenMI and other model coupling standards, ...
DSD-INT 2014 - OpenMI symposium - OpenMI and other model coupling standards, ...
Deltares
Puerto Rico crossing in two days
Puerto Rico crossing in two days
rafael_carrion
Presentation to Leadership Chapel Hill-Carrboro on Blue Ribbon Mentor-Advocate
Presentation to Leadership Chapel Hill-Carrboro on Blue Ribbon Mentor-Advocate
Kristen Smith
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Python is a widely used general-purpose, high-level programming language.Its design philosophy emphasizes code readability, and its syntax allows programmers to express concepts in fewer lines of code than would be possible in languages such as C.The language provides constructs intended to enable clear programs on both a small and large scale.Python supports multiple programming paradigms, including object-oriented, imperative and functional programming or procedural styles. It features a dynamic type system and automatic memory management and has a large and comprehensive standard library.
Python Online From EasyLearning Guru
Python Online From EasyLearning Guru
KCC Software Ltd. & Easylearning.guru
Get to know the usage of Python once you graduate from college. Also when and why you must avoid Python.
The Python outside of your textbook
The Python outside of your textbook
Aniket Prabhu
Girish one year
Girish one year
girish bb
Developers often wonder how to implement a certain functionality (e.g., how to parse XML files) using APIs. Obtaining an API usage sequence based on an API-related natural language query is very helpful in this regard. Given a query, existing approaches utilize information retrieval models to search for matching API sequences. These approaches treat queries and APIs as bags-of-words and lack a deep understanding of the semantics of the query. We propose DeepAPI, a deep learning based approach to generate API usage sequences for a given natural language query. Instead of a bag-of-words assumption, it learns the sequence of words in a query and the sequence of associated APIs. DeepAPI adapts a neural language model named RNN Encoder-Decoder. It encodes a word sequence (user query) into a fixed-length context vector, and generates an API sequence based on the context vector. We also augment the RNN Encoder-Decoder by considering the importance of individual APIs. We empirically evaluate our approach with more than 7 million annotated code snippets collected from GitHub. The results show that our approach generates largely accurate API sequences and outperforms the related approaches.
Deep API Learning (FSE 2016)
Deep API Learning (FSE 2016)
Sung Kim
Middleware fourth unit
Middleware fourth unit
selva kumar
Summer Research Project. Final Presentation 2013
Summer Research Project. Final Presentation 2013
Ojaswa Anand
Benefits of Extensions
Benefits of Extensions
Alexandro Colorado
Basic Presentation On python and it's Keywords..
Presentation on python
Presentation on python
william john
This presentation tells about Python and R, their differences and their interconnection.
Python and r in data science
Python and r in data science
Ravi Ranjan Prasad Karn
How to integrate python into a scala stack
How to integrate python into a scala stack
Fliptop
brf to mathml
brf to mathml
Adarsh Burma
How we can make a more cleaner testable code by applying functional programming ideas in Python
Functional programming ideas in python
Functional programming ideas in python
Manish Tomar
python tutorial from beginner
Python indroduction
Python indroduction
FEG
A presentation by Morteza Zakeri Iran University of Science and Technology Fall 2016
An Introduction to ANTLR
An Introduction to ANTLR
Morteza Zakeri
[PyCon 2014 APAC] How to integrate python into a scala stack to build realtim...
[PyCon 2014 APAC] How to integrate python into a scala stack to build realtim...
Jerry Chou
ZIB use Xeon Phi to achieve their Connected Compenent Labeling strategy #ISC13 #HPC
Connected Component Labeling on Intel Xeon Phi Coprocessors – Parallelization...
Connected Component Labeling on Intel Xeon Phi Coprocessors – Parallelization...
Intel IT Center
Slides for a talk given by Fernando Pérez at PyData Silicon Valley 2013
IPython: A Modern Vision of Interactive Computing (PyData SV 2013)
IPython: A Modern Vision of Interactive Computing (PyData SV 2013)
PyData
Python programming Launguage
Python presentation
Python presentation
gaganapponix
This is the Introduction to Python for Beginners
Introduction to python for Beginners
Introduction to python for Beginners
Sujith Kumar
OpenMI
DSD-INT 2014 - OpenMI symposium - OpenMI and other model coupling standards, ...
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Deltares
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Python Online From EasyLearning Guru
Python Online From EasyLearning Guru
The Python outside of your textbook
The Python outside of your textbook
Girish one year
Girish one year
Deep API Learning (FSE 2016)
Deep API Learning (FSE 2016)
Middleware fourth unit
Middleware fourth unit
Summer Research Project. Final Presentation 2013
Summer Research Project. Final Presentation 2013
Benefits of Extensions
Benefits of Extensions
Presentation on python
Presentation on python
Python and r in data science
Python and r in data science
How to integrate python into a scala stack
How to integrate python into a scala stack
brf to mathml
brf to mathml
Functional programming ideas in python
Functional programming ideas in python
Python indroduction
Python indroduction
An Introduction to ANTLR
An Introduction to ANTLR
[PyCon 2014 APAC] How to integrate python into a scala stack to build realtim...
[PyCon 2014 APAC] How to integrate python into a scala stack to build realtim...
Connected Component Labeling on Intel Xeon Phi Coprocessors – Parallelization...
Connected Component Labeling on Intel Xeon Phi Coprocessors – Parallelization...
IPython: A Modern Vision of Interactive Computing (PyData SV 2013)
IPython: A Modern Vision of Interactive Computing (PyData SV 2013)
Python presentation
Python presentation
Introduction to python for Beginners
Introduction to python for Beginners
DSD-INT 2014 - OpenMI symposium - OpenMI and other model coupling standards, ...
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Puerto Rico crossing in two days
Puerto Rico crossing in two days
rafael_carrion
Presentation to Leadership Chapel Hill-Carrboro on Blue Ribbon Mentor-Advocate
Presentation to Leadership Chapel Hill-Carrboro on Blue Ribbon Mentor-Advocate
Kristen Smith
*
Update on Local Taxes
Update on Local Taxes
Kristen Smith
Presentation on Chamber Social Media Presence
Presentation on Chamber Social Media Presence
Kristen Smith
Presentation to EDU 132 class at UNC-Chapel Hill
Presentation to EDU 132: The Chamber & Networking
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Kristen Smith
SLM (Sample Lifecycle Manager) laboratory workflow and data management software.
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Town and Gown Working Together: Four Women Make It Happen
Town and Gown Working Together: Four Women Make It Happen
Kristen Smith
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Puerto Rico crossing in two days
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Presentation to Leadership Chapel Hill-Carrboro on Blue Ribbon Mentor-Advocate
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Update on Local Taxes
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Presentation on Chamber Social Media Presence
Presentation on Chamber Social Media Presence
Presentation to EDU 132: The Chamber & Networking
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SLM (Sample Lifecycle Manager)
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PyData Meetup Natal April 2024
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https://www.insight-centre.org/content/research-toolbox-data-analysis-python-waternomics-case-study This seminar aims to highlight the flexibility of Python as a useful programming language for everyday tasks in research. It is based on the experience of the presenter in the Waternomics project and research experiments. The overall goal is to share the experience of data access, manipulation, and visualization. The seminar will focus on following main topics and their relevant Python libraries: (1) The Python ecosystem for Data Science (2) Data access with pandas, RDFlib, requests, json (3) Data manipulation with numpy, scipy, statsmodels (4) Data visualization with matplotlib, seaborn, and bokeh (5) Tips and tricks (Jupyter server, pgfplots, latex, pyCharm) (6) Advanced libraries (scikt-learn, pyomo, NLTK) The seminar is expected to use the full slot of the Reading Group session, with opportunities for questions and discussion in between each topic.
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Flink Community Update 2015 June presented 23rd June in Berlin.
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Given on Tuesday, June 23, 2009 at the Greater Cleveland PC Users Group C#/VB.NET SIG. A very basic intro to Python given to a .NET crowd with the assumption of little to no Python experience.
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ApacheCon 2021 Apache Deep Learning 302 Tuesday 18:00 UTC Apache Deep Learning 302 Timothy Spann This talk will discuss and show examples of using Apache Hadoop, Apache Kudu, Apache Flink, Apache Hive, Apache MXNet, Apache OpenNLP, Apache NiFi and Apache Spark for deep learning applications. This is the follow up to previous talks on Apache Deep Learning 101 and 201 and 301 at ApacheCon, Dataworks Summit, Strata and other events. As part of this talk, the presenter will walk through using Apache MXNet Pre-Built Models, integrating new open source Deep Learning libraries with Python and Java, as well as running real-time AI streams from edge devices to servers utilizing Apache NiFi and Apache NiFi - MiNiFi. This talk is geared towards Data Engineers interested in the basics of architecting Deep Learning pipelines with open source Apache tools in a Big Data environment. The presenter will also walk through source code examples available in github and run the code live on Apache NiFi and Apache Flink clusters. Tim Spann is a Developer Advocate @ StreamNative where he works with Apache NiFi, Apache Pulsar, Apache Flink, Apache MXNet, TensorFlow, Apache Spark, big data, the IoT, machine learning, and deep learning. Tim has over a decade of experience with the IoT, big data, distributed computing, streaming technologies, and Java programming. Previously, he was a Principal Field Engineer at Cloudera, a senior solutions architect at AirisData and a senior field engineer at Pivotal. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton on big data, the IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as IoT Fusion, Strata, ApacheCon, Data Works Summit Berlin, DataWorks Summit Sydney, and Oracle Code NYC. He holds a BS and MS in computer science. * https://github.com/tspannhw/ApacheDeepLearning302/ * https://github.com/tspannhw/nifi-djl-processor * https://github.com/tspannhw/nifi-djlsentimentanalysis-processor * https://github.com/tspannhw/nifi-djlqa-processor * https://www.linkedin.com/pulse/2021-schedule-tim-spann/
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What is Python?
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An introduction to Python in science and engineering. The presentation was given by Dr Edward Schofield of Python Charmers (www.pythoncharmers.com) to A*STAR and the Singapore Computational Sciences Club in June 2011.
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Pythonfinalppt 170822121204
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Python Tutorial for beginner & python Basics who need to excited to learn basics of python. Here you will know about python overview
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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 is continued to be a favourite 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.
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Dubai, often portrayed as a shimmering oasis in the desert, faces its own set of challenges, including the occasional threat of flooding. Despite its reputation for opulence and modernity, the emirate is not immune to the forces of nature. In recent years, Dubai has experienced sporadic but significant floods, testing the resilience of its infrastructure and communities. Among the critical lifelines in this bustling metropolis is the Dubai International Airport, a bustling hub that connects the city to the world. This article explores the intersection of Dubai flood events and the resilience demonstrated by the Dubai International Airport in the face of such challenges.
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Keynote 2: APIs in 2030: The Risk of Technological Sleepwalk Paolo Malinverno, Growth Advisor - The Business of Technology Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/
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Dubai, known for its towering skyscrapers, luxurious lifestyle, and relentless pursuit of innovation, often finds itself in the global spotlight. However, amidst the glitz and glamour, the emirate faces its own set of challenges, including the occasional threat of flooding. In recent years, Dubai has experienced sporadic but significant floods, disrupting normalcy and posing unique challenges to its infrastructure. Among the critical nodes in this bustling metropolis is the Dubai International Airport, a vital hub connecting the world. This article delves into the intersection of Dubai flood events and the resilience demonstrated by the Dubai International Airport in the face of such challenges.
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Webinar Recording: https://www.panagenda.com/webinars/why-teams-call-analytics-is-critical-to-your-entire-business Nothing is as frustrating and noticeable as being in an important call and being unable to see or hear the other person. Not surprising then, that issues with Teams calls are among the most common problems users call their helpdesk for. Having in depth insight into everything relevant going on at the user’s device, local network, ISP and Microsoft itself during the call is crucial for good Microsoft Teams Call quality support. To ensure a quick and adequate solution and to ensure your users get the most out of their Microsoft 365. But did you know that ‘bad calls’ are also an excellent indicator of other problems arising? Precisely because it is so noticeable!? Like the canary in the mine, bad calls can be early indicators of problems. Problems that might otherwise not have been noticed for a while but can have a big impact on productivity and satisfaction. Join this session by Christoph Adler to learn how true Microsoft Teams call quality analytics helped other organizations troubleshoot bad calls and identify and fix problems that impacted Teams calls or the use of Microsoft365 in general. See what it can do to keep your users happy and productive! In this session we will cover - Why CQD data alone is not enough to troubleshoot call problems - The importance of attributing call problems to the right call participant - What call quality analytics can do to help you quickly find, fix-, and prevent problems - Why having retrospective detailed insights matters - Real life examples of how others have used Microsoft Teams call quality monitoring to problem shoot problems with their ISP, network, device health and more.
Why Teams call analytics are critical to your entire business
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When you’re building (micro)services, you have lots of framework options. Spring Boot is no doubt a popular choice. But there’s more! Take Quarkus, a framework that’s considered the rising star for Kubernetes-native Java. It always depends on what's best for your situation, but how to choose the best solution if you're comparing 2 frameworks? Both Spring Boot and Quarkus have their positives and negatives. Let us compare the two by live coding a couple of common use cases in Spring Boot and Quarkus. After this talk, you’ll be ready to get started with Quarkus yourself, and know when to select Quarkus or Spring Boot.
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Following the popularity of "Cloud Revolution: Exploring the New Wave of Serverless Spatial Data," we're thrilled to announce this much-anticipated encore webinar. In this sequel, we'll dive deeper into the Cloud-Native realm by uncovering practical applications and FME support for these new formats, including COGs, COPC, FlatGeoBuf, GeoParquet, STAC, and ZARR. Building on the foundation laid by industry leaders Michelle Roby of Radiant Earth and Chris Holmes of Planet in the first webinar, this second part offers an in-depth look at the real-world application and behind-the-scenes dynamics of these cutting-edge formats. We will spotlight specific use-cases and workflows, showcasing their efficiency and relevance in practical scenarios. Discover the vast possibilities each format holds, highlighted through detailed discussions and demonstrations. Our expert speakers will dissect the key aspects and provide critical takeaways for effective use, ensuring attendees leave with a thorough understanding of how to apply these formats in their own projects. Elevate your understanding of how FME supports these cutting-edge technologies, enhancing your ability to manage, share, and analyze spatial data. Whether you're building on knowledge from our initial session or are new to the serverless spatial data landscape, this webinar is your gateway to mastering cloud-native formats in your workflows.
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Corporate and higher education. Two industries that, in the past, have had a clear divide with very little crossover. The difference in goals, learning styles and objectives paved the way for differing learning technologies platforms to evolve. Now, those stark lines are blurring as both sides are discovering they have content that’s relevant to the other. Join Tammy Rutherford as she walks through the pros and cons of corporate and higher ed collaborating. And the challenges of these different technology platforms working together for a brighter future.
Corporate and higher education May webinar.pptx
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This Slide deck talk about how FHIR is being used in Ayushman Bharat Digital Mission (ABDM). It introduces the readers to ABDM and also to FHIR Documents paradigm. This is part of FHIR India community Basics learning initiative.
Introduction to use of FHIR Documents in ABDM
Introduction to use of FHIR Documents in ABDM
Kumar Satyam
ICT role in 21 century education. How to ICT help in education
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presentation ICT roal in 21st century education
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ICT role in education and it's challenges. In which we learn about ICT, it's impact, benefits and challenges.
ICT role in 21st century education and its challenges
ICT role in 21st century education and its challenges
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JAM, the future of Polkadot.
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
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In this keynote, Asanka Abeysinghe, CTO,WSO2 will explore the shift towards platformless technology ecosystems and their importance in driving digital adaptability and innovation. We will discuss strategies for leveraging decentralized architectures and integrating diverse technologies, with a focus on building resilient, flexible, and future-ready IT infrastructures. We will also highlight WSO2's roadmap, emphasizing our commitment to supporting this transformative journey with our evolving product suite.
Platformless Horizons for Digital Adaptability
Platformless Horizons for Digital Adaptability
WSO2
RailsConf 2024 - Insights Gained From Developing A Hybrid Application Using Turbo-Native and Strada.
JohnPollard-hybrid-app-RailsConf2024.pptx
JohnPollard-hybrid-app-RailsConf2024.pptx
JohnPollard37
💥 You’re lucky! We’ve found two different (lead) developers that are willing to share their valuable lessons learned about using UiPath Document Understanding! Based on recent implementations in appealing use cases at Partou and SPIE. Don’t expect fancy videos or slide decks, but real and practical experiences that will help you with your own implementations. 📕 Topics that will be addressed: • Training the ML-model by humans: do or don't? • Rule-based versus AI extractors • Tips for finding use cases • How to start 👨🏫👨💻 Speakers: o Dion Morskieft, RPA Product Owner @Partou o Jack Klein-Schiphorst, Automation Developer @Tacstone Technology
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
UiPathCommunity
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Deepika Singh
Effective data discovery is crucial for maintaining compliance and mitigating risks in today's rapidly evolving privacy landscape. However, traditional manual approaches often struggle to keep pace with the growing volume and complexity of data. Join us for an insightful webinar where industry leaders from TrustArc and Privya will share their expertise on leveraging AI-powered solutions to revolutionize data discovery. You'll learn how to: - Effortlessly maintain a comprehensive, up-to-date data inventory - Harness code scanning insights to gain complete visibility into data flows leveraging the advantages of code scanning over DB scanning - Simplify compliance by leveraging Privya's integration with TrustArc - Implement proven strategies to mitigate third-party risks Our panel of experts will discuss real-world case studies and share practical strategies for overcoming common data discovery challenges. They'll also explore the latest trends and innovations in AI-driven data management, and how these technologies can help organizations stay ahead of the curve in an ever-changing privacy landscape.
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc
The CNIC Information System is a comprehensive database managed by the National Database and Registration Authority (NADRA) of Pakistan. It serves as the primary source of identification for Pakistani citizens and residents, containing vital information such as name, date of birth, address, and biometric data.
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CNIC Information System with Pakdata Cf In Pakistan
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Introduction to use of FHIR Documents in ABDM
Introduction to use of FHIR Documents in ABDM
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presentation ICT roal in 21st century education
ICT role in 21st century education and its challenges
ICT role in 21st century education and its challenges
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Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
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JohnPollard-hybrid-app-RailsConf2024.pptx
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
Mcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot Model
Mcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot Model
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TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
CNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In Pakistan
Pycon 2011
1.
2.
2010 attendance: ~1100
-> 2011 attendance: ~1500
3.
4.
Scientific Computing
5.
Networking
6.
System Administration
7.
Rapid Prototyping
8.
Systems Testing
9.
10.
Great documentation
11.
Large
standard lib
12.
Tons
of high quality 3rd party libraries
13.
Useful collection of
built-in data types
14.
Cool features:
15.
lambdas, list comprehensions, generators,
properties, decorators
16.
Avoids pitfalls of other dynamic languages:
17.
Namespaced
18.
Everything is
an object
19.
Strong typing
with no implicit or explicit casting
20.
Runtime error
on undefined variables
21.
22.
Python 3.2 was
released in February
23.
Many 1st level
dependencies have been ported to 3
24.
Expect more rapid
adoption as the number of 3rd party packages grows
25.
PSF is providing
funding for open source projects to port
26.
27.
2.7 – 3.2
28.
PyPy (the new
hotness): Pure python implementation
29.
Uses a JIT
to get better performance than CPython
30.
Supports 2.7
31.
Jython: Python on
JVM
32.
Supports 2.5
33.
IronPython: Python on
CLR
34.
Supports 2.7
35.
36.
Huge community
37.
Reusable application modules
38.
Runs on GAE
39.
PostgreSQL, SQLite, and
MySQL still popular database options
40.
MongoDB and CouchDB
are popular NoSQL options
41.
Memcache or Varnish
for caching
42.
Apache with mod_wsgi
for server
43.
JSON for AJAX
44.
Pip and virtualenv
used for dependency management
45.
46.
Large scale parallelized
simulations
47.
90% Python, computationally
intensive parts in C or Fortran
48.
File handling, process
management, networking, gui, data visualization, testing
49.
50.
Write tasks in
Python
51.
Tasks are executed
on local or remote slaves
52.
Handle results asynchronously
53.
Integrates with Django
54.
mpi4py: Python interface
for MPI
55.
Used at Argonne
National Laboratory on 100K node machine
56.
MPI is still
around
57.
mrjob: Python interface
to Hadoop
58.
Write map/reduce functions
in Python
59.
60.
61.
Javascript for people
who know Python
62.
Supporting All Versions
of Python All the Time with Tox
63.
Linguistics of Twitter
64.
Mrjob: Distributed Computing
for Everyone
65.
Extreme Network Programming
with Python and Linux
66.
Rapid Python used
on Big Data to Discover Human Genetic Variation
67.
Python for High
Performance Computing
68.
The Data Structures
of Python
69.
Documentation Driven Development
70.
Genetic Programming in
Python
71.
Exhibition of Atrocity
72.
API Design: Lessons
Learned
73.
What would you
do with an ast?
74.
Through the Side
Channel: Timing and Implementation Attacks in Python
75.
An outsider’s look
at co-routines
76.
Best Practices for
Impossible Deadlines