https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Volume rendering 3D volume data (medical CT scans) in Unity3D.
Covering the following topics:
- Raymarching
- Maximum Intensity Projection
- Direct Volume Rendering with compositing
- Isosurface rendering
- Transfer functions
- 2D Transfer Functions
- Slice rendering
Source code here: https://github.com/mlavik1/UnityVolumeRendering
Part 1 of the Deep Learning Fundamentals Series, this session discusses the use cases and scenarios surrounding Deep Learning and AI; reviews the fundamentals of artificial neural networks (ANNs) and perceptrons; discuss the basics around optimization beginning with the cost function, gradient descent, and backpropagation; and activation functions (including Sigmoid, TanH, and ReLU). The demos included in these slides are running on Keras with TensorFlow backend on Databricks.
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Volume rendering 3D volume data (medical CT scans) in Unity3D.
Covering the following topics:
- Raymarching
- Maximum Intensity Projection
- Direct Volume Rendering with compositing
- Isosurface rendering
- Transfer functions
- 2D Transfer Functions
- Slice rendering
Source code here: https://github.com/mlavik1/UnityVolumeRendering
Part 1 of the Deep Learning Fundamentals Series, this session discusses the use cases and scenarios surrounding Deep Learning and AI; reviews the fundamentals of artificial neural networks (ANNs) and perceptrons; discuss the basics around optimization beginning with the cost function, gradient descent, and backpropagation; and activation functions (including Sigmoid, TanH, and ReLU). The demos included in these slides are running on Keras with TensorFlow backend on Databricks.
Winning data science competitions, presented by Owen ZhangVivian S. Zhang
<featured> Meetup event hosted by NYC Open Data Meetup, NYC Data Science Academy. Speaker: Owen Zhang, Event Info: http://www.meetup.com/NYC-Open-Data/events/219370251/
An overview of Deep Learning With Neural Networks. Use cases of Deep learning and it's development. Basic introduction tp the layers of Neural Networks.
발표자: 이활석 (Naver Clova)
발표일: 2017.11.
(현) NAVER Clova Vision
(현) TFKR 운영진
개요:
최근 딥러닝 연구는 지도학습에서 비지도학습으로 급격히 무게 중심이 옮겨지고 있습니다.
특히 컴퓨터 비전 기술 분야에서는 지도학습에 해당하는 이미지 내에 존재하는 정보를 찾는 인식 기술에서,
비지도학습에 해당하는 특정 정보를 담는 이미지를 생성하는 기술인 생성 기술로 연구 동향이 바뀌어 가고 있습니다.
본 세미나에서는 생성 기술의 두 축을 담당하고 있는 VAE(variational autoencoder)와 GAN(generative adversarial network) 동작 원리에 대해서 간략히 살펴 보고, 관련된 주요 논문들의 결과를 공유하고자 합니다.
딥러닝에 대한 지식이 없더라도 생성 모델을 학습할 수 있는 두 방법론인 VAE와 GAN의 개념에 대해 이해하고
그 기술 수준을 파악할 수 있도록 강의 내용을 구성하였습니다.
Dr Murari Mandal from NUS presented as part of 3 days OpenPOWER Industry summit about Robustness in Deep learning where he talked about AI Breakthroughs , Performance improments in AI models , Adversarial attacks , Attacks on semantic segmentation , Attacs on object detector , Defending Against adversarial attacks and many other areas.
I have implemented various optimizers (gradient descent, momentum, adam, etc.) based on gradient descent using only numpy not deep learning framework like TensorFlow.
Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.
In this talk, Dmitry shares his approach to feature engineering which he used successfully in various Kaggle competitions. He covers common techniques used to convert your features into numeric representation used by ML algorithms.
Winning data science competitions, presented by Owen ZhangVivian S. Zhang
<featured> Meetup event hosted by NYC Open Data Meetup, NYC Data Science Academy. Speaker: Owen Zhang, Event Info: http://www.meetup.com/NYC-Open-Data/events/219370251/
An overview of Deep Learning With Neural Networks. Use cases of Deep learning and it's development. Basic introduction tp the layers of Neural Networks.
발표자: 이활석 (Naver Clova)
발표일: 2017.11.
(현) NAVER Clova Vision
(현) TFKR 운영진
개요:
최근 딥러닝 연구는 지도학습에서 비지도학습으로 급격히 무게 중심이 옮겨지고 있습니다.
특히 컴퓨터 비전 기술 분야에서는 지도학습에 해당하는 이미지 내에 존재하는 정보를 찾는 인식 기술에서,
비지도학습에 해당하는 특정 정보를 담는 이미지를 생성하는 기술인 생성 기술로 연구 동향이 바뀌어 가고 있습니다.
본 세미나에서는 생성 기술의 두 축을 담당하고 있는 VAE(variational autoencoder)와 GAN(generative adversarial network) 동작 원리에 대해서 간략히 살펴 보고, 관련된 주요 논문들의 결과를 공유하고자 합니다.
딥러닝에 대한 지식이 없더라도 생성 모델을 학습할 수 있는 두 방법론인 VAE와 GAN의 개념에 대해 이해하고
그 기술 수준을 파악할 수 있도록 강의 내용을 구성하였습니다.
Dr Murari Mandal from NUS presented as part of 3 days OpenPOWER Industry summit about Robustness in Deep learning where he talked about AI Breakthroughs , Performance improments in AI models , Adversarial attacks , Attacks on semantic segmentation , Attacs on object detector , Defending Against adversarial attacks and many other areas.
I have implemented various optimizers (gradient descent, momentum, adam, etc.) based on gradient descent using only numpy not deep learning framework like TensorFlow.
Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.
In this talk, Dmitry shares his approach to feature engineering which he used successfully in various Kaggle competitions. He covers common techniques used to convert your features into numeric representation used by ML algorithms.
NIPS2013読み会: More Effective Distributed ML via a Stale Synchronous Parallel P...Shohei Hido
NIPS2013読み会の発表資料です。
Qirong Ho et al, "More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server", NIPS2013.
http://media.nips.cc/nipsbooks/nipspapers/paper_files/nips26/631.pdf
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
3. My Roots
Images from BYU Mers Lab
Saturday, March 17, 12
4. Science led to Python
2
⇢0 (2⇡f ) Ui (a, f ) = [Cijkl (a, f ) Uk,l (a, f )],j
Raja Muthupillai
Richard Ehman
1997
Armando Manduca
Saturday, March 17, 12
9. Brief History
Person Package Year
Matrix Object
Jim Fulton 1994
in Python
Jim Hugunin Numeric 1995
Perry Greenfield, Rick
White, Todd Miller Numarray 2001
Travis Oliphant NumPy 2005
Saturday, March 17, 12
10. Found Python and Numeric in 1997
I was a fairly proficient MATLAB user, but it was not memory efficient enough.
Loved the expressive syntax of Python
Loved the fact that slicing didn’t make copies
Loved the existing multiple data-types
Loved how much more flexible it was to
extend than MATLAB was
Loved that I could read the source code and
extend it
Saturday, March 17, 12
11. First problem: Efficient Data Input
“It’s All About the Data”
Reference Counting Essay
TableIO http://www.python.org/doc/essays/refcnt/
April 1998 May 1998
Michael A. Miller Guido van Rossum
NumPyIO
June 1998
Saturday, March 17, 12
12. First problem: Efficient Data Input
“It’s All About the Data”
Reference Counting Essay
TableIO http://www.python.org/doc/essays/refcnt/
April 1998 May 1998
Michael A. Miller Guido van Rossum
NumPyIO
June 1998
Saturday, March 17, 12
13. Early pieces of SciPy
fftw wrappers cephesmodule
June 1998 November 1998
stats.py
December 1998
Gary
Strangman
Saturday, March 17, 12
14. 1999 : Early SciPy emerges
Discussions on the matrix-sig from 1997 to 1999 wanting a complete data analysis
environment: Paul Barrett, Joe Harrington, Perry Greenfield, Paul Dubois, Konrad Hinsen,
and others. Activity in 1998, led to increased interest in 1999.
In response on 15 Jan, 1999, I posted to matrix-sig a list of routines I felt needed to be
present and began wrapping / writing in earnest. On 6 April 1999, I announced I would
be creating this uber-package which eventually became SciPy
Gaussian quadrature 5 Jan 1999
cephes 1.0 30 Jan 1999
sigtools 0.40 23 Feb 1999
Numeric docs March 1999
cephes 1.1 9 Mar 1999 Plotting??
multipack 0.3 13 Apr 1999
Helper routines 14 Apr 1999 Gist
multipack 0.6 (leastsq, ode, fsolve, 29 Apr 1999 XPLOT
quad)
DISLIN
sparse plan described 30 May 1999
Gnuplot
multipack 0.7 14 Jun 1999
SparsePy 0.1
cephes 1.2 (vectorize)
5 Nov 1999
29 Dec 1999
Helping with f2py
Saturday, March 17, 12
15. Early 2000 : Numeric needs
• Memory mapped arrays
• Rank-0 arrays or scalars
• Handling indirect indexing: a[[10,5,7]]
• Handling masked indexing: a[[True, False, False]
• More attributes to N-d arrays
• “Record arrays”
Saturday, March 17, 12
16. Early contributors in 1999
Hosting of first Multipack CVS repository (June 1999)
Amazing makefiles
Interface to FITPACK
Wrote f2py as he watched my brute-force approach (July 1999)
Pearu Peterson
(IPP) Early IPython interactive environment (27 Apr 1999)
Matlab file reader (24 Apr 1999)
Janko Hauser
Created windows binaries of multipack, cephesmodule,
fftw, and signaltools (June 1999 while still in high school!)
Robert Kern
Saturday, March 17, 12
17. Early contributors in 1999
Hosting of first Multipack CVS repository (June 1999)
Amazing makefiles
Interface to FITPACK
Wrote f2py as he watched my brute-force approach (July 1999)
Pearu Peterson
(IPP) Early IPython interactive environment (27 Apr 1999)
Matlab file reader (24 Apr 1999)
Janko Hauser
Created windows binaries of multipack, cephesmodule,
fftw, and signaltools (June 1999 while still in high school!)
Robert Kern
Saturday, March 17, 12
22. SciPy 2001 Travis Oliphant
optimize
sparse
interpolate
integrate
special
signal
stats Founded in 2001 with Travis Vaught
fftpack
misc
Eric Jones
weave
cluster
Pearu Peterson
GA*
linalg
interpolate
f2py
Saturday, March 17, 12
25. STSCI leads out with Numarray
Perry Greenfield
J. Todd Miller
Rick White
Paul Barrett
Saturday, March 17, 12
26. Numarray released in 2003
• too slow for small arrays
• incomplete implementation for ufuncs
• minimal Numeric code re-use
• lots of very nice things, though (e.g. memory
maps, fast code for large arrays, better
sorting algorithms)
Saturday, March 17, 12
27. Split in the community
Numeric
SciPy
Numarray
ndimage
others
Saturday, March 17, 12
29. started January 2005
NumPy
Version 1.0 October 2006
Key contributions from:
Numarray
Numeric
Chuck Harris
Robert Kern
David Cooke
Pierre GM
Saturday, March 17, 12
30. NumPy in Python
Q: When is NumPy going to be part of the
core Python?
A: Data structure is in Python as PEP 3118
Saturday, March 17, 12
33. Community effort many, many others --- forgive me!
• Chuck Harris
• Pauli Virtanen
• David Cournapeau
• Stefan van der Walt
• Jarrod Millman
• Josef Perktold
• Anne Archibald
• Dag Sverre Seljebotn
• Robert Kern
• Matthew Brett
• Warren Weckesser
• Ralf Gommers
• Joe Harrington --- Documentation effort
• Andrew Straw --- www.scipy.org
Saturday, March 17, 12
35. NumPy Essentials
• Data: the array object
– slicing
– shapes and strides
– data-type generality
• Fast Math:
– vectorization
– broadcasting
– aggregations
Saturday, March 17, 12
36. Zen of NumPy
• strided is better than scattered
• contiguous is better than strided
• descriptive is better than imperative
• array-oriented is better than object-oriented
• broadcasting is a great idea
• vectorized is better than an explicit loop
• unless it’s too complicated --- then use Cython / weave
• think in higher dimensions
Saturday, March 17, 12
38. Experiences Conulsting
• Basically, I was a consultant and manager of
consultants for 4 years.
• It kept the lights on at home, but gave me minimal
time for NumPy.
• Silver lining was that I learned exactly what “big-data”
problems big companies have and saw first-hand that
numerous “little” improvements need to be made to
NumPy which will allow it to have a larger impact on
helping to manage the world’s data.
Saturday, March 17, 12
39. Putting Science back in CS
• Much of the software stack is for systems
programming --- C++, Java, .NET, ObjC, web
- Complex numbers?
- Vectorization primitives?
• Array-based programming has been
supplanted by Object-oriented programming
• Software stack for scientists is not as helpful
as it should be
• Fortran is still where many scientists end up
Saturday, March 17, 12
40. APL : the first array-oriented language
• Appeared in 1964
• Originated by Ken Iverson
• Direct descendants (J, K, Matlab) are still
used heavily and people pay a lot of money
for them APL
• NumPy is a descendent J
K Matlab
Numeric
NumPy
Saturday, March 17, 12
41. Conway’s game of Life
• Dead cell with exactly 3 live neighbors
will come to life
• A live cell with 2 or 3 neighbors will
survive
• With too few or too many neighbors, the
cell dies
Saturday, March 17, 12
43. Conway’s Game of Life
APL
NumPy
Initialization
Update Step
Saturday, March 17, 12
44. Improvements needed
• NDArray improvements
• Indexes (esp. for Structured arrays)
• SQL front-end
• Multi-level, hierarchical labels
• selection via mappings (labeled arrays)
• Memory spaces (array made up of regions)
• Distributed arrays (global array)
• Compressed arrays
• Standard distributed persistance
• fancy indexing as view and optimizations
• streaming arrays
Saturday, March 17, 12
45. Improvements needed
• Dtype improvements
• Enumerated types (including dynamic enumeration)
• Derived fields
• Specification as a class (or JSON)
• Pointer dtype (i.e. C++ object, or varchar)
• Finishing datetime
• Missing data with both bit-patterns and mask
• Parameterized field names
Saturday, March 17, 12
46. Example of Object Dtype
@np.dtype
class Stock(np.DType):
symbol = np.Str(4)
open = np.Int(2)
close = np.Int(2)
high = np.Int(2)
low = np.Int(2)
@np.Int(2)
def mid(self):
return (self.high + self.low) / 2.0
Saturday, March 17, 12
47. Improvements needed
• Ufunc improvements
• Generalized ufuncs support more than just
contiguous arrays
• Specification of ufuncs in Python
• Move most dtype “array functions” to ufuncs
• Unify error-handling for all computations
• Allow lazy-evaluation and remote computation ---
streaming and generator data
• Structured and string dtype ufuncs
• Multi-core and GPU optimized ufuncs
• Group-by reduction
Saturday, March 17, 12
48. Improvements needed
• Miscellaneous improvements
• ABI-management
• Eventual Move to C++ library (NDLib)
• NDLib could serve as base for Javascript and other
high-level languages
• Integration with LLVM
• Possible dtype / shape / stride unification into a
“dimension protocol”
• Remote computation
• Fast I/O for CSV and Excel
Saturday, March 17, 12
49. NumPy Users
• Want to be able to write Python to get fast
code that works on arrays and scalars
• Need access to a boat-load of C-extensions
(NumPy is just the beginning)
PyPy doesn’t cut it for us!
Saturday, March 17, 12
50. Ufuncs
Saturday, March 17, 12
Generalized
UFuncs
Python
Function
Window
Kernel
Funcs
Function-
based
Indexing
Memory
Dynamic compilation
Filters
Dynamic
Compilation
NumPy Runtime
I/O Filters
Reduction
Filters
Computed
Columns
function pointer
51. SciPy needs a Python compiler
optimize integrate
special ode
writing more of SciPy at high-level
Saturday, March 17, 12
52. Numba -- a Python compiler
• Replays byte-code on a stack with simple type-
inference
• Translates to LLVM (using LLVM-py)
• Uses LLVM for code-gen
• Resulting C-level function-pointer can be
inserted into NumPy run-time
• Understands NumPy arrays
• Is NumPy / SciPy aware
Saturday, March 17, 12
53. NumPy + Mamba = Numba
Python Function Machine Code
LLVM-PY
LLVM 3.1
ISPC OpenCL OpenMP CUDA CLANG
Intel AMD Nvidia Apple
Saturday, March 17, 12
60. NumFOCUS
Num(Py) Foundation for Open Code for Usable Science
Saturday, March 17, 12
61. NumFOCUS
• Mission
• To initiate and support educational programs
furthering the use of open source software in
science.
• To promote the use of high-level languages and
open source in science, engineering, and math
research
• To encourage reproducible scientific research
Saturday, March 17, 12
62. NumFOCUS
• Activites
• Sponsor sprints and conferences
• Provide scholarships and grants
• Pay for documentation development and basic
course development
• Work with domain-specific organizations
• Raise funds from industries using Python and
NumPy
Saturday, March 17, 12
63. NumFOCUS
• Directors
• Jarrod Millman
• Fernando Perez
• Travis Oliphant
• Perry Greenfield
• John Hunter
• Members
• Basically people who donate for now. In time, a
body that elects directors.
Saturday, March 17, 12