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GitRecruit is a tool that uses machine learning and natural language processing to automate talent searching on GitHub. It constructs tf-idf vectors from the contents of README files for over 110,000 GitHub repositories. These vectors are used to calculate cosine similarity between repositories to find projects with similar topics. The tool was tested on 256 README files and achieved a mean average precision of 0.39, significantly better than random. The creator, Yinghan Fu, developed GitRecruit during their fellowship and has experience in machine learning, natural language processing, and collaborative filtering.
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Description
The GooFit highly parallel fitting package for GPUs and CPUs has been substantially upgraded in the past year. Python bindings have been added to allow simple access to the fitting configuration, setup, and execution. A Python tool to write custom GooFit code given a (compact and elegant) MINT3/AmpGen amplitude description allows the corresponding C++ code to be written quickly and correctly. New PDFs have been added. The most recent release was built on top of the December 2017 2.0 release that added easier builds, new platforms, and a more robust and efficient underlying function evaluation engine.
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has recently seen a boost of development which culminated in the
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To simplify Minuit’s use as a standalone CMake package, for projects
like iminuit and GooFit, a new standalone build system
was implemented for Minuit2, and has been
included in the latest release of ROOT. This system uses modern CMake
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scripts, and the system even supports building from inside ROOT.
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- H5py allows HDF5 files to be manipulated as if they were Python dictionaries, with dataset names as keys and arrays as values.
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The GooFit package provides physicists a simple, familiar syntax for manipulating probability density functions and performing fits, but is highly optimized for data analysis on NVIDIA GPUs and multithreaded CPU backends. GooFit is being updated to version 2.0, bringing a host of new features. A completely revamped and redesigned build system makes GooFit easier to install, develop with, and run on virtually any system. Unit testing, continuous integration, and advanced logging options are improving the stability and reliability of the system. Developing new PDFs now uses standard CUDA terminology and provides a lower barrier for new users. The system now has built-in support for multiple graphics cards or nodes using MPI, and is being tested on a wide range of different systems.
GooFit also has significant improvements in performance on some GPU architectures due to optimized memory access. Support for time-dependent four body amplitude analyses has also been added.
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When your aplication becomes a bit messy and you have a lot of points of entry to it with many sources of truth - maybe it is time to think about pushing you business logic to PostgreSQL?
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Similar to GitRecruit final 1
Python standard library & list of important libraries
We know that a module is a file with some Python code, and a package is a directory for sub packages and modules. But the line between a package and a Python library is quite blurred.
A Python library is a reusable chunk of code that you may want to include in your programs/ projects. Compared to languages like C++ or C, a Python libraries do not pertain to any specific context in Python. Here, a ‘library’ loosely describes a collection of core modules. Essentially, then, a library is a collection of modules. A package is a library that can be installed using a package manager like rubygems or npm.
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Professor Ion Stoica, UC Berkeley RISELab
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
Deep Learning Frameworks 2019 | Which Deep Learning Framework To Use | Deep L...
The document discusses several deep learning frameworks including TensorFlow, Keras, PyTorch, Theano, Deep Learning 4 Java, Caffe, Chainer, and Microsoft CNTK. TensorFlow was developed by Google Brain Team and uses dataflow graphs to process data. Keras is a high-level neural network API that runs on top of TensorFlow, Theano, and CNTK. PyTorch was designed for flexibility and speed using CUDA and C++ libraries. Theano defines and evaluates mathematical expressions involving multi-dimensional arrays efficiently in Python. Deep Learning 4 Java integrates with Hadoop and Apache Spark to bring AI to business environments. Caffe focuses on image detection and classification using C++ and Python. Chainer was developed in collaboration with several companies
DHT_62196cbe17eefc645ce6794676313372.pptx
This document discusses IoT application development and the key steps involved: sensor data collection, communication and networking, processing, and analysis. It outlines some of the most popular programming languages used for IoT development in 2015 and 2017, including Python. Several essential Python libraries for IoT are then described, such as mraa for GPIO access, sockets for networking, mysqldb for databases, Numpy for scientific computing, and matplotlib, pandas, opencv, tkinter, tensorflow, requests, and paho-mqtt for various functions like data visualization, analysis, image processing, GUI development, machine learning, HTTP calls, and MQTT protocol usage. The document concludes with an explanation of collecting data from a DHT sensor.
Logging for Production Systems in The Container Era
Logging for Production Systems in The Container Era discusses how to effectively collect and analyze logs and metrics in microservices-based container environments. It introduces Fluentd as a centralized log collection service that supports pluggable input/output, buffering, and aggregation. Fluentd allows collecting logs from containers and routing them to storage systems like Kafka, HDFS and Elasticsearch. It also supports parsing, filtering and enriching log data through plugins.
Tips and tricks for data science projects with Python
Python has become the most widely used language for machine learning and data science projects due to its simplicity and versatility.
Furthermore, developers get to put all their effort into solving an Machine Learning or data science problem instead of focusing on the technical aspects of the language.
For this purpose, Python provides access to great libraries and frameworks for AI and machine learning (ML), flexibility and platform independence
In this talk I will try to get a selection of libraries and frameworks that can help us introduce in the Machine Learning world and answer the question that all people is doing, What makes Python the best programming language for machine learning?
Python ml
I am shubham sharma graduated from Acropolis Institute of technology in Computer Science and Engineering. I have spent around 2 years in field of Machine learning. I am currently working as Data Scientist in Reliance industries private limited Mumbai. Mainly focused on problems related to data handing, data analysis, modeling, forecasting, statistics and machine learning, Deep learning, Computer Vision, Natural language processing etc. Area of interests are Data Analytics, Machine Learning, Machine learning, Time Series Forecasting, web information retrieval, algorithms, Data structures, design patterns, OOAD.
Role of python in hpc
Dr. REEJA S R gave a talk on high performance computing (HPC) and Python. She discussed what HPC is, when it is needed, and what it includes. She also covered the history of computer architectures for HPC, including vector computers, massively parallel processors, symmetric multiprocessors, and clusters. Additionally, she explained what Python is, why it is useful for HPC, and some of the libraries that can help with HPC tasks like NumPy, SciPy, and MPI4py. Finally, she discussed some challenges with Python for HPC and ways to improve performance, such as through the PyMPI, Pynamic, PyTrilinos, ODIN, and Seamless libraries
Session 2
This document provides an agenda for a training session on AI and data science. The session is divided into two units: data science and data visualization. Key Python libraries that will be covered for data science include NumPy, Pandas, and Matplotlib. NumPy will be used to create and manipulate multi-dimensional arrays. Pandas allows users to work with labeled and relational data. Matplotlib enables data visualization through graphs and plots. The session aims to provide knowledge of core data science libraries and demonstrate data exploration techniques using these packages.
Python 3.5: An agile, general-purpose development language.
This is the presentation I gave about Python 3.5 to my research group. It was my intention to introduce the Python language to some of the new members who don't know or have little knowledge about the language.
Scientific Computing @ Fred Hutch
At the technology meeting of the Association of Independent Research Centers (http://airi.org): An overview of recent Scientific Computing activities at Fred Hutch, Seattle
ACM Sunnyvale Meetup.pdf
Modern machine learning (ML) workloads, such as deep learning and large-scale model training, are compute-intensive and require distributed execution. Ray is an open-source, distributed framework from U.C. Berkeley’s RISELab that easily scales Python applications and ML workloads from a laptop to a cluster, with an emphasis on the unique performance challenges of ML/AI systems. It is now used in many production deployments.
This talk will cover Ray’s overview, architecture, core concepts, and primitives, such as remote Tasks and Actors; briefly discuss Ray native libraries (Ray Tune, Ray Train, Ray Serve, Ray Datasets, RLlib); and Ray’s growing ecosystem.
Through a demo using XGBoost for classification, we will demonstrate how you can scale training, hyperparameter tuning, and inference—from a single node to a cluster, with tangible performance difference when using Ray.
The takeaways from this talk are :
Learn Ray architecture, core concepts, and Ray primitives and patterns
Why Distributed computing will be the norm not an exception
How to scale your ML workloads with Ray libraries:
Training on a single node vs. Ray cluster, using XGBoost with/without Ray
Hyperparameter search and tuning, using XGBoost with Ray Tune
Inferencing at scale, using XGBoost with/without Ray
Introduction to Python Programming
This document provides an introduction to Python programming. It discusses that Python is an interpreted, object-oriented, high-level programming language with simple syntax. It then covers the need for programming languages, different types of languages, and translators like compilers, interpreters, assemblers, linkers, and loaders. The document concludes by discussing why Python is popular for web development, software development, data science, and more.
License Plate Recognition System using Python and OpenCV
License plate recognition (LPR) is a type of technology, mainly software, that enables computer systems to read automatically the registration number (license number) of vehicles from digital pictures.
WRENCH: Workflow Management System Simulation Workbench
WRENCH enables novel avenues for scientific workflow use, research, development, and education. WRENCH capitalizes on recent and critical advances in the state of the art of distributed platform/application simulation. WRENCH builds on top of the open-source SimGrid simulation framework. SimGrid enables the simulation of large-scale distributed applications in a way that is accurate (via validated simulation models), scalable (low ratio of simulation time to simulated time, ability to run large simulations on a single computer with low compute, memory, and energy footprints), and expressive (ability to simulate arbitrary platform, application, and execution scenarios). WRENCH provides directly usable high-level simulation abstractions using SimGrid as a foundation. More information on https://wrench-project.org
In a nutshell, WRENCH makes it possible to:
- Prototype implementations of Workflow Management System (WMS) components and underlying algorithms;
- Quickly, scalably, and accurately simulate arbitrary workflow and platform scenarios for a simulated WMS implementation; and
- Run extensive experimental campaigns to conclusively compare workflow executions, platform architectures, and WMS algorithms and designs.
Anaconda Python KNIME & Orange Installation
This document provides an overview and installation instructions for machine learning basics using various tools and libraries. It discusses installing and setting up Orange, KNIME, Anaconda, and related Python libraries. Key steps include downloading installers, setting paths, defining workspaces, installing extensions, and creating workflows in Orange and KNIME. Popular cheminformatics and deep learning libraries supported include RDKit, DeepChem, numpy, and scikit-learn.
Robot Framework with Python | Edureka
Youtube Link: https://youtu.be/d-KWz7euLlc
** Edureka Python Certification Training: https://www.edureka.co/data-science-python-certification-course **
This Edureka PPT on 'Robot Framework With Python' explains the various aspects of robot framework in python with a use case showing web testing using selenium library.
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
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
Castbox: https://castbox.fm/networks/505?country=in
Acceleration of Statistical Detection of Zero-day Malware in the Memory Dump ...
This paper focuses on the anticipatory enhancement of methods of detecting stealth software. Cyber security detection tools are insufficiently powerful to reveal the most recent cyber-attacks which use malware. In this paper, we will present first an idea of the highest stealth malware, as this is the most complicated scenario for detection because it combines both existing anti-forensic techniques together with their potential improvements. Second, we present new detection methods, which are resilient to this hidden prototype. To help solve this detection challenge, we have analyzed Windows memory content using a new method of Shannon Entropy calculation; methods of digital photogrammetry; the Zipf–Mandelbrot law, as well as by disassembling the memory content and analyzing the output. Finally, we present an idea and architecture of the software tool, which uses CUDA-enabled GPU hardware to speed-up memory forensics. All three ideas are currently a work in progress.
Getting More Out of the Node.js, PHP, and Python Agents - AppSphere16
Hear from our product management and engineering teams about three topics that will help you unlock more value from our dynamic languages agents:
• Diagnosing a slow business transaction in Node.js
• Using the agent APIs to create custom transactions and exit calls
• Getting the Java proxy out of your Docker containers and connecting multiple agents to one proxy
For more information, go to: www.appdynamics.com
Improving your team's source code searching capabilities - Voxxed Thessalonik...
Nikos Katirtzis gave a presentation on improving source code searching capabilities. He discussed why source code search engines are needed as developers spend more time reading code than writing it. He then compared popular code search engines like Searchcode Server, Hound, Zoekt, and Sourcegraph. Finally, he described HApiDoc, a service developed by Hotels.com that mines API usage examples from client source code using an approach called CLAMS.
Similar to GitRecruit final 1 (20)
Python standard library & list of important libraries
Python standard library & list of important libraries
Deep Learning Frameworks 2019 | Which Deep Learning Framework To Use | Deep L...
Deep Learning Frameworks 2019 | Which Deep Learning Framework To Use | Deep L...
Logging for Production Systems in The Container Era
Logging for Production Systems in The Container Era
Tips and tricks for data science projects with Python
Tips and tricks for data science projects with Python
Python 3.5: An agile, general-purpose development language.
Python 3.5: An agile, general-purpose development language.
License Plate Recognition System using Python and OpenCV
License Plate Recognition System using Python and OpenCV
WRENCH: Workflow Management System Simulation Workbench
WRENCH: Workflow Management System Simulation Workbench
Acceleration of Statistical Detection of Zero-day Malware in the Memory Dump ...
Acceleration of Statistical Detection of Zero-day Malware in the Memory Dump ...
Getting More Out of the Node.js, PHP, and Python Agents - AppSphere16
Getting More Out of the Node.js, PHP, and Python Agents - AppSphere16
Improving your team's source code searching capabilities - Voxxed Thessalonik...
Improving your team's source code searching capabilities - Voxxed Thessalonik...
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Meet up Milano 14 _ Axpo Italia_ Migration from Mule3 (On-prem) to.pdf
Quattordicesimo Meetup di Milano, tenutosi a Milano il 23 Maggio 2024 dalle ore 17:00 alle ore 18:30 in presenza e da remoto.
Abbiamo parlato di come Axpo Italia S.p.A. ha ridotto il technical debt migrando le proprie APIs da Mule 3.9 a Mule 4.4 passando anche da on-premises a CloudHub 1.0.
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Should Repositories Participate in the Fediverse?
Presentation for OR2024 making the case that repositories could play a part in the "fediverse" of distributed social applications
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2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
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2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
教育部文凭学历认证认证的用途：
如果您计划在国内发展那么办理国内教育部认证是必不可少的。事业性用人单位如银行国企公务员在您应聘时都会需要您提供这个认证。其他私营 #外企企业无需提供！办理教育部认证所需资料众多且烦琐所有材料您都必须提供原件我们凭借丰富的经验帮您快速整合材料让您少走弯路。
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2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
教育部文凭学历认证认证的用途：
如果您计划在国内发展那么办理国内教育部认证是必不可少的。事业性用人单位如银行国企公务员在您应聘时都会需要您提供这个认证。其他私营 #外企企业无需提供！办理教育部认证所需资料众多且烦琐所有材料您都必须提供原件我们凭借丰富的经验帮您快速整合材料让您少走弯路。
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2开始安排制作皇家墨尔本理工大学毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
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6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
APNIC Foundation, presented by Ellisha Heppner at the PNG DNS Forum 2024
Ellisha Heppner, Grant Management Lead, presented an update on APNIC Foundation to the PNG DNS Forum held from 6 to 10 May, 2024 in Port Moresby, Papua New Guinea.
Explore-Insanony: Watch Instagram Stories Secretly
Instagram has become one of the most popular social media platforms, allowing people to share photos, videos, and stories with their followers. Sometimes, though, you might want to view someone's story without them knowing.
Discover the benefits of outsourcing SEO to India
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2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）
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GitRecruit final 1
- 1. GitRecruit Recruit the right tech talent using Github Insight Data Science Fellow: Yinghan Fu
- 2. Tech companies compete for talent • Recruiting is difficult and expensive. • Companies use Github for code repositories. • GitRecruit can automate talent search.
- 3. Algorithm-using README files to find similar repositories NLTK
- 4. Algorithm-using README files to find similar repositories NLTK -- the Natural Language Toolkit -- is a suite of open source Python modules, data sets and tutorials supporting research and development in Natural Language Processing. NLTK README
- 5. Algorithm-using README files to find similar repositories NLTK README tf-idf vector ݈ܽ݊݃݁݃ܽݑ ݄݊ݐݕ ݈݊ܽܽݎݑݐ ݐ݈ ݐ݈݅݇ݐ ⋮ 1.8 2.4 2.4 2 2 ⋮
- 6. Algorithm-using README files to find similar repositories NLTK README tf-idf vector Scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license. The project was started in 2007 by David Cournapeau as a Google Summer of Matplotlib is a python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. matplotlib can be used in python ~110,000 repository README files ~70% pull requests NumPy is the fundamental package needed for scientific computing with Python. This package contains: a powerful N- dimensional array object . sophisticated (broadcasting) functions ݈ܽ݊݃݁݃ܽݑ ݄݊ݐݕ ݈݊ܽܽݎݑݐ ݐ݈ ݐ݈݅݇ݐ ⋮ 1.8 2.4 2.4 2 2 ⋮
- 7. Algorithm-using README files to find similar repositories NLTK README tf-idf vector ~110,000 repository README files ~70% pull requests ݈ܽ݊݃݁݃ܽݑ ݄݊ݐݕ ݈݊ܽܽݎݑݐ ݐ݈ ݂ܿ݅݅ݐ݊݁݅ܿݏ ⋮ 0 0 0 2.5 2.3 2.8 0 0 0 0 3.1 0 5.4 4.3 6.7 ⋮ ⋮ ⋮ ݈ܽ݊݃݁݃ܽݑ ݄݊ݐݕ ݈݊ܽܽݎݑݐ ݐ݈ ݐ݈݅݇ݐ ⋮ 1.8 2.4 2.4 2 2 ⋮
- 8. Algorithm-using README files to find similar repositories ܰ ܭܶܮ 0.5 0.3 0.8 Cosine similarity
- 9. Algorithm-using README files to find similar repositories ܰ ܭܶܮ 0.5 0.3 0.8 Cosine similarity
- 10. Optimization and benchmark show the search engine is effective Database Driver Audio Text Processing Training 133 repository readme files Optimize mean average precision Testing 123 repository readme files Mean average precision 0.39 (random 0.09, p-value<0.00001) …………… …………… …………… …………… …………… …………… …………… …………… …………… …………… …………… …………… …………… …………… …………… …………… …………… …………… …………… …………… …………… …………… …………… …………… …………… …………… …………… …………… …………… …………… …………… …………… …………… …………… …………… …………… ....
- 11. About me, Yinghan Fu
- 12. About me, Yinghan Fu • GitRecruit – MySQL, Python, NLTK, scikit-learn etc. • Machine learning – C++, Python • Dynamic programming algorithm – C++ • User-user/item-item collaborative filtering – Python, mrjob, MongoDB
- 13. The search engine can concentrate python packages from the same category
- 14. Github activities are highly concentrated 16 mil repos 6 mil users 2.7 mil pull requests merged 0.4 mil repos have any pull requests merged
- 15. tf-idf vectorization ܶܨ = 1 + log (݂ݐ௧,ௗ) ܨܦܫ = log ܰ ݂݀௧
- 16. Parameters optimized • Number of words included • Minimum document frequency • Maximum document frequency • Sublinear term frequency • Cosine similarity • Maximum n-gram