Probabilistic Programming allows very flexible creation of custom probabilistic models and is mainly concerned with insight and learning from your data. The approach is inherently Bayesian so we can specify priors to inform and constrain our models and get uncertainty estimation in form of a posterior distribution. Using MCMC sampling algorithms we can draw samples from this posterior to very flexibly estimate these models. PyMC3 and Stan are the current state-of-the-art tools to construct and estimate these models.
One major drawback of sampling, however, is that it's often very slow, especially for high-dimensional models. That's why more recently, variational inference algorithms have been developed that are almost as flexible as MCMC but much faster. Instead of drawing samples from the posterior, these algorithms instead fit a distribution (e.g. normal) to the posterior turning a sampling problem into and optimization problem. ADVI -- Automatic Differentation Variational Inference -- is implemented in PyMC3 and Stan, as well as a new package called Edward which is mainly concerned with Variational Inference.
In every programming languages recursion is widely used, its very useful for programmer for designing a code.All detail information provided by this slide please go and throw that.
Apresentação sobre Criptografia baseada em reticulados (lattices), realizada no contexto da disciplina de Post-Quantum Cryptography do PPGCC da UFSC.
Versão odp: http://coenc.td.utfpr.edu.br/~giron/presentations/aula_lattice.odp
Probabilistic Programming allows very flexible creation of custom probabilistic models and is mainly concerned with insight and learning from your data. The approach is inherently Bayesian so we can specify priors to inform and constrain our models and get uncertainty estimation in form of a posterior distribution. Using MCMC sampling algorithms we can draw samples from this posterior to very flexibly estimate these models. PyMC3 and Stan are the current state-of-the-art tools to construct and estimate these models.
One major drawback of sampling, however, is that it's often very slow, especially for high-dimensional models. That's why more recently, variational inference algorithms have been developed that are almost as flexible as MCMC but much faster. Instead of drawing samples from the posterior, these algorithms instead fit a distribution (e.g. normal) to the posterior turning a sampling problem into and optimization problem. ADVI -- Automatic Differentation Variational Inference -- is implemented in PyMC3 and Stan, as well as a new package called Edward which is mainly concerned with Variational Inference.
In every programming languages recursion is widely used, its very useful for programmer for designing a code.All detail information provided by this slide please go and throw that.
Apresentação sobre Criptografia baseada em reticulados (lattices), realizada no contexto da disciplina de Post-Quantum Cryptography do PPGCC da UFSC.
Versão odp: http://coenc.td.utfpr.edu.br/~giron/presentations/aula_lattice.odp
Lattice-Based Cryptography: CRYPTANALYSIS OF COMPACT-LWEPriyanka Aash
Destructive and constructive methods in lattice-based cryptography will be discussed. Topic 1: Cryptanalysis of Compact-LWE Authors: Jonathan Bootle; Mehdi Tibouchi; Keita Xagawa Topic 2: Two-message Key Exchange with Strong Security from Ideal Lattices Authors: Zheng Yang; Yu Chen; Song Luo
(Source: RSA Conference USA 2018)
this is a briefer overview about the Big O Notation. Big O Notaion are useful to check the Effeciency of an algorithm and to check its limitation at higher value. with big o notation some examples are also shown about its cases and some functions in c++ are also described.
Presentation of the article "Profiling Java Programs for Parallelism" of C. Hammacher, K. Streit, S. Hack and A. Zeller. All Rights for text are Reserved by authors of this paper.
Date of presentation: July 2011
For preparing my slides I take pictures and some other information from the internet and I try to use only legal one. But if I did not notice something and you have Rights for any kind of this information and do not want to see it in the presentation please let me know and I will remove it from the slides as fast as possible or remove the slides themselves. Thanks for your collaboration.
International Journal of Managing Information Technology (IJMIT)IJMIT JOURNAL
We present an improved SPFA algorithm for the single source shortest path problem. For a random graph, the empirical average time complexity is O(|E|), where |E| is the number of edges of the input network. SPFA maintains a queue of candidate vertices and add a vertex to the queue only if that vertex is relaxed. In the improved SPFA, MinPoP principle is employed to improve the quality of the queue. We theoretically analyse the advantage of this new algorithm and experimentally demonstrate that the algorithm is efficient
An improved spfa algorithm for single source shortest path problem using forw...IJMIT JOURNAL
We present an improved SPFA algorithm for the single source shortest path problem. For a random graph,
the empirical average time complexity is O(|E|), where |E| is the number of edges of the input network.
SPFA maintains a queue of candidate vertices and add a vertex to the queue only if that vertex is relaxed.
In the improved SPFA, MinPoP principle is employed to improve the quality of the queue. We theoretically
analyse the advantage of this new algorithm and experimentally demonstrate that the algorithm is efficient.
An improved spfa algorithm for single source shortest path problem using forw...IJMIT JOURNAL
We present an improved SPFA algorithm for the single source shortest path problem. For a random graph,
the empirical average time complexity is O(|E|), where |E| is the number of edges of the input network.
SPFA maintains a queue of candidate vertices and add a vertex to the queue only if that vertex is relaxed.
In the improved SPFA, MinPoP principle is employed to improve the quality of the queue. We theoretically
analyse the advantage of this new algorithm and experimentally demonstrate that the algorithm is efficient.
Adversarial Reinforced Learning for Unsupervised Domain Adaptationtaeseon ryu
안녕하세요 딥러닝 논문읽기 모임입니다 오늘 업로드된 논문 리뷰 영상은 2021 WACB 에서 발표된 Adversarial Reinforced Learning for Unsupervised Domain Adaptation 라는 제목의 논문입니다.
데이터 분류의 자동화를 위해서는 많은양의 학습데이터가 필요합니다. 그렇기에 레이블이 존재하는 데이터로 학습이 끝난 모델을 재활용해서 새로운 도메인에 적용하는 연구인 도메인 어뎁션 분야는 많은 각광을 받고 있습니다.
논문의 특징으로는 크게 세가지를 둘 수 있습니다.
첫 번째로 본 논문에서는 GAN을 이용하여 비지도 방식으로 도메인 어뎁션이 가능한 프레임워크를 제안하였습니다 여기서 이제 강화학습 모델은 소스와 타겟
도메인간 가장 최적의 피처쌍을 선택하는데 사용됩니다
두 번째로 레이블링 되지않은 타겟 도메인에서 가장 적합한 피처를 찾아내기 위해
소스와 타겟간 상관관계를 보상으로 적용하는 정책을 개발하였습니다
마지막으로 제안된 적대적 강화학습 모델을 소스와 타겟 도메인간
최소화하는 피처쌍의 탐색과 각 도메인의 거리 분포상태의
Alignment 학습을 통해 소타대비 이제 성능을 향상 하였습니다
논문에 대한 디테일한 리뷰를 펀디멘탈팀 이근배님이 많은 도움 주셨습니다!
Lattice-Based Cryptography: CRYPTANALYSIS OF COMPACT-LWEPriyanka Aash
Destructive and constructive methods in lattice-based cryptography will be discussed. Topic 1: Cryptanalysis of Compact-LWE Authors: Jonathan Bootle; Mehdi Tibouchi; Keita Xagawa Topic 2: Two-message Key Exchange with Strong Security from Ideal Lattices Authors: Zheng Yang; Yu Chen; Song Luo
(Source: RSA Conference USA 2018)
this is a briefer overview about the Big O Notation. Big O Notaion are useful to check the Effeciency of an algorithm and to check its limitation at higher value. with big o notation some examples are also shown about its cases and some functions in c++ are also described.
Presentation of the article "Profiling Java Programs for Parallelism" of C. Hammacher, K. Streit, S. Hack and A. Zeller. All Rights for text are Reserved by authors of this paper.
Date of presentation: July 2011
For preparing my slides I take pictures and some other information from the internet and I try to use only legal one. But if I did not notice something and you have Rights for any kind of this information and do not want to see it in the presentation please let me know and I will remove it from the slides as fast as possible or remove the slides themselves. Thanks for your collaboration.
International Journal of Managing Information Technology (IJMIT)IJMIT JOURNAL
We present an improved SPFA algorithm for the single source shortest path problem. For a random graph, the empirical average time complexity is O(|E|), where |E| is the number of edges of the input network. SPFA maintains a queue of candidate vertices and add a vertex to the queue only if that vertex is relaxed. In the improved SPFA, MinPoP principle is employed to improve the quality of the queue. We theoretically analyse the advantage of this new algorithm and experimentally demonstrate that the algorithm is efficient
An improved spfa algorithm for single source shortest path problem using forw...IJMIT JOURNAL
We present an improved SPFA algorithm for the single source shortest path problem. For a random graph,
the empirical average time complexity is O(|E|), where |E| is the number of edges of the input network.
SPFA maintains a queue of candidate vertices and add a vertex to the queue only if that vertex is relaxed.
In the improved SPFA, MinPoP principle is employed to improve the quality of the queue. We theoretically
analyse the advantage of this new algorithm and experimentally demonstrate that the algorithm is efficient.
An improved spfa algorithm for single source shortest path problem using forw...IJMIT JOURNAL
We present an improved SPFA algorithm for the single source shortest path problem. For a random graph,
the empirical average time complexity is O(|E|), where |E| is the number of edges of the input network.
SPFA maintains a queue of candidate vertices and add a vertex to the queue only if that vertex is relaxed.
In the improved SPFA, MinPoP principle is employed to improve the quality of the queue. We theoretically
analyse the advantage of this new algorithm and experimentally demonstrate that the algorithm is efficient.
Adversarial Reinforced Learning for Unsupervised Domain Adaptationtaeseon ryu
안녕하세요 딥러닝 논문읽기 모임입니다 오늘 업로드된 논문 리뷰 영상은 2021 WACB 에서 발표된 Adversarial Reinforced Learning for Unsupervised Domain Adaptation 라는 제목의 논문입니다.
데이터 분류의 자동화를 위해서는 많은양의 학습데이터가 필요합니다. 그렇기에 레이블이 존재하는 데이터로 학습이 끝난 모델을 재활용해서 새로운 도메인에 적용하는 연구인 도메인 어뎁션 분야는 많은 각광을 받고 있습니다.
논문의 특징으로는 크게 세가지를 둘 수 있습니다.
첫 번째로 본 논문에서는 GAN을 이용하여 비지도 방식으로 도메인 어뎁션이 가능한 프레임워크를 제안하였습니다 여기서 이제 강화학습 모델은 소스와 타겟
도메인간 가장 최적의 피처쌍을 선택하는데 사용됩니다
두 번째로 레이블링 되지않은 타겟 도메인에서 가장 적합한 피처를 찾아내기 위해
소스와 타겟간 상관관계를 보상으로 적용하는 정책을 개발하였습니다
마지막으로 제안된 적대적 강화학습 모델을 소스와 타겟 도메인간
최소화하는 피처쌍의 탐색과 각 도메인의 거리 분포상태의
Alignment 학습을 통해 소타대비 이제 성능을 향상 하였습니다
논문에 대한 디테일한 리뷰를 펀디멘탈팀 이근배님이 많은 도움 주셨습니다!
In the quest of improving the quality of education, Flexudy leverages the
power of AI to help people learn more efficiently.
During the talk, I will show how we trained an automatic extractive text
summarizer based on concepts from Reinforcement Learning, Deep Learning and Natural Language Processing. Also, I will talk about how we use pre-trained NLP models to generate simple questions for self-assessment.
This is a single day course, allows the learner to get experience with the basic details of deep learning, first half is building a network using python/numpy only and the second half we build the more advanced netwrok using TensorFlow/Keras.
At the end you will find a list of usefull pointers to continue.
course git: https://gitlab.com/eshlomo/EazyDnn
Deep dive into the mathematics and algorithms of neural nets. Covers the sigmoid activation function, cross-entropy loss function, gradient descent and the derivatives used in back propagation.
Jay Yagnik at AI Frontiers : A History Lesson on AIAI Frontiers
We have reached a remarkable point in history with the evolution of AI, from applying this technology to incredible use cases in healthcare, to addressing the world's biggest humanitarian and environmental issues. Our ability to learn task-specific functions for vision, language, sequence and control tasks is getting better at a rapid pace. This talk will survey some of the current advances in AI, compare AI to other fields that have historically developed over time, and calibrate where we are in the relative advancement timeline. We will also speculate about the next inflection points and capabilities that AI can offer down the road, and look at how those might intersect with other emergent fields, e.g. Quantum computing.
The term Machine Learning was coined by Arthur Samuel in 1959, an American pioneer in the field of computer gaming and artificial intelligence, and stated that “it gives computers the ability to learn without being explicitly programmed”. Machine Learning is the latest buzzword floating around. It deserves to, as it is one of the most interesting subfields of Computer Science. So what does Machine Learning really mean? Let’s try to understand Machine Learning
Building graphs to discover information by David Martínez at Big Data Spain 2015Big Data Spain
The basic challenge of a data scientist is to unveil information from raw data. Traditional machine learning algorithms have treated “pure” data analytics situations that should comply with a set of restrictions, such as access to labels, a clear prediction objective… However, the reality in practice shows that, due to the wide spread of data science nowadays, the exception is the norm and it is usual to encounter situations that depend on gathering information from raw data which lacks any kind of structure, or objective that classic approaches assume. In these situations, building a graph that encodes the information we are trying to unveil is the most intuitive place to start or even the only one feasible when we lack any field knowledge or previously stated aim. Unfortunately, building a graph when the number of nodes is huge from scratch is a challenging task computationally, and requires some approximations to make it feasible. In this review, we will talk about the most standard way of building those graphs in practice, and how to exploit them to solve data science tasks.
Session presented at Big Data Spain 2015 Conference
15th Oct 2015
Kinépolis Madrid
http://www.bigdataspain.org
Event promoted by: http://www.paradigmatecnologico.com
Abstract: http://www.bigdataspain.org/program/thu/slot-11.html#spch11.2
Manifold Blurring Mean Shift algorithms for manifold denoising, report, 2012Florent Renucci
(General) To retrieve a clean dataset by deleting outliers.
(Computer Vision) the recovery of a digital image that has been contaminated by additive white Gaussian noise.
From Lab to Factory: Creating value with dataPeadar Coyle
One of the biggest challenges in Data Science, is deploying Machine Learning models. There are cultural and technological challenges and I'll explain these and share some insights/ solutions.
Data Science - despite involving many technical skills is not only about these skills. I presented a quick lighting talk, discussing general business skills such as project scoping, communication, negotiation, etc. I think these are neglected skills in our conversation about data.
Big Data and Internet of Things for ManagersPeadar Coyle
A non-technical introduction to the real-world impacts of 'big data' included case studies from industries including Food, fashion, recruitment, logistics and others.
Introduction to Spark: Or how I learned to love 'big data' after all.Peadar Coyle
Slides from a talk I will give in early 2016 at the Luxembourg Data Science Meetup. Aim is to give an introduction to Apache Spark, from a Machine Learning experts point of view. Based on various other tutorials out there. This will be aimed at non-specialists.
A technical talk discussing how to use the Markov Chain Monte Carlo methods inPyMC3 to deliver novel Bayesian Statistical models. Our case study is how to infer the strengths of Rugby teams from the Six Nations. This talk was delivered at the University of Cambridge in 2015.
From Lab to Factory: Or how to turn data into valuePeadar Coyle
We've all heard of 'big data' or data science, but how do we convert these trends into actual business value. I share case studies, and technology tips and talk about the challenges of the data science process. This is all based on two years of in-the-field research of deploying models, and going from prototypes to production.
These are slides from my talk at PyCon Ireland 2015
How can Data Science benefit your business?Peadar Coyle
A talk I gave to an audience of non-specialists in Luxembourg in late 2014 on data science and the opportunities in sectors including HR, Energy, Marketing and Supply Chain
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
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
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
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
6. World’s 1st
peer-to-peer
lending platform
in 2004
£2.5 billion
lent to date,
and our growth is
accelerating
246,000
people have taken
a Zopa loan
59,000
actively invest
through Zopa
7.
8.
9. What is PyMC3?
• Probabilistic Programming in Python
• At release stage – so ready for production
• Theano based
• Powerful sampling algorithms
• Powerful model syntax
• Recent improvements include Gaussian Processes and enhanced Variational
Inference
11. Who uses it?
• Used widely in academia and industry
• https://github.com/pymc-devs/pymc3/wiki/PyMC3-Testimonials
• https://scholar.google.de/scholar?hl=en&as_sdt=0,5&sciodt=0,5&cites=6936955228135731
011&scipsc=&authuser=1&q=&scisbd=1
12. What is a Bayesian approach?
• The Bayesian world-view interprets probability as a measure of believability in an event, that
is, how confident are we that an event will occur.
• The Frequentist approach/ view is – considers that probability is the long-run frequency of
events.
• This doesn’t make much sense for say Presidential elections!
• Bayesians interpret a probability as a measure of beliefs. This allows us all to have different
priors.
13. Are Frequentist methods wrong?
• NO
• Least squares regression, LASSO regression and expectation-maximization are all powerful
and fast in many areas.
• Bayesian methods complement these techniques by solving problems that these approaches
can’t
• Or by illuminating the underlying system with more flexible modelling.
14. Example – Let’s look at text message data
This data comes from Cameron Davidson-Pilon from his own text message
history. He wrote the examples and the book this talk is based on. It’s cited at the
end.
15. Example – Inferring text message data
- A Poisson random variable is a very appropriate model for this type of count
data.
- The math will be something like C_{i} ∼ Poisson(λ)
- We don’t know what the lambda is – what is it?
16. Example – Inferring text message data (continued)
- It looks like the rate is higher later in the observation period.
- We’ll represent this with a ‘switchpoint’ – it’s a bit like how we write a delta
function (we use a day which we call tau)
- λ={λ1 if t<τ
- {λ2 if t≥τ
17. Priors – Or beliefs
- We call alpha a hyper-parameter or parent variable. In literal terms, it is a
parameter that influences other parameters.
- Alternatively, we could have two priors – one for each λi -- EXERCISE
We are interested in inferring the unknown λs. To use Bayesian inference, we need
to assign prior probabilities to the different possible values of λ What would be
good prior probability distributions for λ1 and λ2?
Recall that λ can be any positive number. As we saw earlier, the exponential
distribution provides a continuous density function for positive numbers, so it
might be a good choice for modelling λi But recall that the exponential distribution
takes a parameter of its own, so we'll need to include that parameter in our
model. Let's call that parameter α.
λ1∼Exp(α)
λ2∼Exp(α)
18. Priors - Continued
- We don’t care what our prior distribution (or integral) for the unknown variables
looks like.
- It’s probably intractable – so needs a method to solve.
- And we care about the posterior distribution
- What about τ?
- Due to the noisiness of the data, it’s difficult to pick out a priori where τ
might have occurred. We’ll pick a uniform prior belief to every possible
day. This is equivalent to saying
τ ∼ DiscreteUniform(1,70)
- This implies that the P(τ=k) = 1/70
19. The philosophy of Probabilistic Programming:
Our first hammer PyMC3
Another way of thinking about this: unlike a traditional program, which only
runs in the forward directions, a probabilistic program is run in both the
forward and backward direction. It runs forward to compute the
consequences of the assumptions it contains about the world (i.e., the model
space it represents), but it also runs backward from the data to constrain the
possible explanations. In practice, many probabilistic programming systems
will cleverly interleave these forward and backward operations to efficiently
home in on the best explanations. -- Beau Cronin – sold a Probabilistic
Programming focused company to Salesforce
20. Let’s specify our variables
import pymc3 as pm import theano.tensor as tt
with pm.Model() as model:
alpha = 1.0/count_data.mean() # Recall count_data is the
# variable that holds our txt counts
lambda_1 = pm.Exponential("lambda_1", alpha)
lambda_2 = pm.Exponential("lambda_2", alpha)
tau = pm.DiscreteUniform("tau", lower = 0, upper=n_count_data - 1)
21. Let’s create the switchpoint and add in
observations
with model:
idx = np.arange(n_count_data) # Index
lambda_ = pm.math.switch(tau >= idx, lambda_1, lambda_2)
with model:
observation = pm.Poisson("obs", lambda_, observed=count_data)
All our variables so far are random variables. We aren’t fixing any variables yet.
The variable observation combines our data (count_data), with our proposed data-
generation schema, given by the variable lambda_, through the observed keyword.
22. Let’s learn something
with model:
trace = pm.sample(10000, tune=5000)
We can think of the above code as a learning step. The machinery we
use is called Markov Chain Monte Carlo (MCMC), which is a whole
workshop. Let’s consider it a magic trick that helps us solve these
complicated formula.
This technique returns thousands of random variables from the
posterior distributions of λ1,λ2 and τ.
We can plot a histogram of the random variables to see what the
posterior distributions look like.
On next slide, we collect the samples (called traces in the MCMC
literature) into histograms.
23. The trace code
lambda_1_samples = trace['lambda_1’]
lambda_2_samples = trace['lambda_2’]
tau_samples = trace['tau']
We’ll leave out the plotting code – you can check in
the notebooks.
25. Interpretation
• The Bayesian methodology returns a distribution.
• We now have distributions to describe the unknown λ1,λ2 and τ
• We can see that the plausible values for the parameters are: λ1 is around 18, and λ2 is
around 23. The posterior distributions of the two lambdas are clearly distinct, indicating
that it is indeed likely that there was a change in the user’s text-message behaviour.
• Our analysis also returned a distribution τ. It’s posterior distribution is discrete. We can see
that near day 45, there was a 50% chance that the user’s behaviour changed. This confirms
that a change occurred because had no changed occurred tau would be more spread out.
We see that only a few days make any sense as potential transition points.
26. Why would I want to sample from the posterior?
• Entire books are devoted to explaining why.
• We’ll muse the posterior samples to answer the following question: what is expected
number of texts at day t, 0≤t≤70? Recall that the expected value of a Poisson variable is
equal to it’s parameter λ. Therefore the question is equivalent to what is the expected value
of λ at time t?
• In our code, let i index samples from the posterior distributions. Given a day t, we average
over all possible λi for the day t, using λi = λ1,i if t < τi (that is, if the behaviour change has
not yet occurred), else we use λi = λ2,i
27. Analysis results
- Our analysis strongly supports believing the users’ behaviour did change.
Otherwise the two lambdas would be closer in value.
- The change was sudden rather than gradual – we see this from tau’s strongly
peaked posterior distribution.
- It turns out the 45th day was Christmas and the book author was moving cities.
28. We introduced Bayesian Methods
Bayesian methods are about beliefs
PyMC3 allows building generative models
We get uncertainty estimates for free
We can add domain knowledge in priors