By popular demand, here is a case study of my first Kaggle competition from about a year ago. Hope you find it useful. Thank you again to my fantastic team.
The world of Machine Learning and Computer Intelligence is coming and is a fast-moving train. Faithful to the Atwood's Law, ML also comes to JavaScript. But there is no such thing as a ‘best language for ML’ and it all depends on what you want to build, what is your background and most important - why you get involved in machine learning. This session is about demystifying and bringing machine learning into the browser.
Fairly Measuring Fairness In Machine LearningHJ van Veen
We look at a case and two research papers on measuring discrimination in machine learning models for extending credit. Presentation given as part of the Sao Paulo Machine Learning Meetup, theme "Ethics in Data Science".
The world of Machine Learning and Computer Intelligence is coming and is a fast-moving train. Faithful to the Atwood's Law, ML also comes to JavaScript. But there is no such thing as a ‘best language for ML’ and it all depends on what you want to build, what is your background and most important - why you get involved in machine learning. This session is about demystifying and bringing machine learning into the browser.
Fairly Measuring Fairness In Machine LearningHJ van Veen
We look at a case and two research papers on measuring discrimination in machine learning models for extending credit. Presentation given as part of the Sao Paulo Machine Learning Meetup, theme "Ethics in Data Science".
H2O World 2015
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Pareto-Optimal Search-Based Software Engineering (POSBSE): A Literature SurveyAbdel Salam Sayyad
Paper presented at the 2nd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE’13), San Francisco, USA. May 2013.
AI at scale requires a perfect storm of data, algorithms and cloud infrastructure. Modern deep learning requires large amounts of training data. We develop methods that improve data collection, aggregation and augmentation. This involves active learning, partial feedback, crowdsourcing, and generative models. We analyze large-scale machine learning methods for distributed training. We show that gradient quantization can yield best of both the worlds: accuracy and communication efficiency. We extend matrix methods to higher dimensions using tensor algebraic techniques and show superior performance. Finally, at AWS, we are developing robust software frameworks and AI cloud services at all levels of the stack.
Techniques for Context-Aware and Cold-Start RecommendationsMatthias Braunhofer
Context-aware recommender systems better identify interesting items for users by adapting their suggestions to the specific contextual situations, e.g., to the current weather, if an excursion is to be recommended . But, the cold-start problem may jeopardise the quality of the recommendations: for users, items or contextual situations that are new to the system, recommendations are hard to compute. We have developed a number of novel techniques to tame this problem, and in particular, new hybrid algorithms that combine several, simpler, algorithms in order to exploit their strengths and avoid their weaknesses. We have also developed algorithms for actively identifying the most useful preference information to ask the user in order to bootstrap the system. Our results obtained from a series of offline and online experiments reveal that the proposed techniques can effectively alleviate the cold-start problem of context-aware recommender systems.
An introductory course on building ML applications with primary focus on supervised learning. Covers the typical ML application cycle - Problem formulation, data definitions, offline modeling, platform design. Also, includes key tenets for building applications.
Note: This is an old slide deck. The content on building internal ML platforms is a bit outdated and slides on the model choices do not include deep learning models.
Winning Kaggle 101: Introduction to StackingTed Xiao
An Introduction to Stacking by Erin LeDell, from H2O.ai
Presented as part of the "Winning Kaggle 101" event, hosted by Machine Learning at Berkeley and Data Science Society at Berkeley. Special thanks to the Berkeley Institute of Data Science for the venue!
H2O.ai: http://www.h2o.ai/
ML@B: ml.berkeley.edu
DSSB: http://dssberkeley.org
BIDS: http://bids.berkeley.edu/
Instance Space Analysis for Search Based Software EngineeringAldeida Aleti
Search-Based Software Engineering is now a mature area with numerous techniques developed to tackle some of the most challenging software engineering problems, from requirements to design, testing, fault localisation, and automated program repair. SBSE techniques have shown promising results, giving us hope that one day it will be possible for the tedious and labour intensive parts of software development to be completely automated, or at least semi-automated. In this talk, I will focus on the problem of objective performance evaluation of SBSE techniques. To this end, I will introduce Instance Space Analysis (ISA), which is an approach to identify features of SBSE problems that explain why a particular instance is difficult for an SBSE technique. ISA can be used to examine the diversity and quality of the benchmark datasets used by most researchers, and analyse the strengths and weaknesses of existing SBSE techniques. The instance space is constructed to reveal areas of hard and easy problems, and enables the strengths and weaknesses of the different SBSE techniques to be identified. I will present on how ISA enabled us to identify the strengths and weaknesses of SBSE techniques in two areas: Search-Based Software Testing and Automated Program Repair. Finally, I will end my talk with future directions of the objective assessment of SBSE techniques.
The performance of deep neural networks improves with more annotated data. The problem is that the budget for annotation is limited. One solution to this is active learning, where a model asks human to annotate data that it perceived as uncertain. A variety of recent methods have been proposed to apply active learning to deep networks but most of them are either designed specific for their target tasks or computationally inefficient for large networks. In this paper, we propose a novel active learning method that is simple but task-agnostic, and works efficiently with the deep networks. We attach a small parametric module, named “loss prediction module,” to a target network, and learn it to predict target losses of unlabeled inputs. Then, this module can suggest data that the target model is likely to produce a wrong prediction. This method is task-agnostic as networks are learned from a single loss regardless of target tasks. We rigorously validate our method through image classification, object detection, and human pose estimation, with the recent network architectures. The results demonstrate that our method consistently outperforms the previous methods over the tasks
The Power of Auto ML and How Does it WorkIvo Andreev
Automated ML is an approach to minimize the need of data science effort by enabling domain experts to build ML models without having deep knowledge of algorithms, mathematics or programming skills. The mechanism works by allowing end-users to simply provide data and the system automatically does the rest by determining approach to perform particular ML task. At first this may sound discouraging to those aiming to the “sexiest job of the 21st century” - the data scientists. However, Auto ML should be considered as democratization of ML, rather that automatic data science.
In this session we will talk about how Auto ML works, how is it implemented by Microsoft and how it could improve the productivity of even professional data scientists.
Predictive Model and Record Description with Segmented Sensitivity Analysis (...Greg Makowski
Describing a predictive data mining model can provide a competitive advantage for solving business problems with a model. The SSA approach can also provide reasons for the forecast for each record. This can help drive investigations into fields and interactions during a data mining project, as well as identifying "data drift" between the original training data, and the current scoring data. I am working on open source version of SSA, first in R.
H2O World 2015
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Pareto-Optimal Search-Based Software Engineering (POSBSE): A Literature SurveyAbdel Salam Sayyad
Paper presented at the 2nd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE’13), San Francisco, USA. May 2013.
AI at scale requires a perfect storm of data, algorithms and cloud infrastructure. Modern deep learning requires large amounts of training data. We develop methods that improve data collection, aggregation and augmentation. This involves active learning, partial feedback, crowdsourcing, and generative models. We analyze large-scale machine learning methods for distributed training. We show that gradient quantization can yield best of both the worlds: accuracy and communication efficiency. We extend matrix methods to higher dimensions using tensor algebraic techniques and show superior performance. Finally, at AWS, we are developing robust software frameworks and AI cloud services at all levels of the stack.
Techniques for Context-Aware and Cold-Start RecommendationsMatthias Braunhofer
Context-aware recommender systems better identify interesting items for users by adapting their suggestions to the specific contextual situations, e.g., to the current weather, if an excursion is to be recommended . But, the cold-start problem may jeopardise the quality of the recommendations: for users, items or contextual situations that are new to the system, recommendations are hard to compute. We have developed a number of novel techniques to tame this problem, and in particular, new hybrid algorithms that combine several, simpler, algorithms in order to exploit their strengths and avoid their weaknesses. We have also developed algorithms for actively identifying the most useful preference information to ask the user in order to bootstrap the system. Our results obtained from a series of offline and online experiments reveal that the proposed techniques can effectively alleviate the cold-start problem of context-aware recommender systems.
An introductory course on building ML applications with primary focus on supervised learning. Covers the typical ML application cycle - Problem formulation, data definitions, offline modeling, platform design. Also, includes key tenets for building applications.
Note: This is an old slide deck. The content on building internal ML platforms is a bit outdated and slides on the model choices do not include deep learning models.
Winning Kaggle 101: Introduction to StackingTed Xiao
An Introduction to Stacking by Erin LeDell, from H2O.ai
Presented as part of the "Winning Kaggle 101" event, hosted by Machine Learning at Berkeley and Data Science Society at Berkeley. Special thanks to the Berkeley Institute of Data Science for the venue!
H2O.ai: http://www.h2o.ai/
ML@B: ml.berkeley.edu
DSSB: http://dssberkeley.org
BIDS: http://bids.berkeley.edu/
Instance Space Analysis for Search Based Software EngineeringAldeida Aleti
Search-Based Software Engineering is now a mature area with numerous techniques developed to tackle some of the most challenging software engineering problems, from requirements to design, testing, fault localisation, and automated program repair. SBSE techniques have shown promising results, giving us hope that one day it will be possible for the tedious and labour intensive parts of software development to be completely automated, or at least semi-automated. In this talk, I will focus on the problem of objective performance evaluation of SBSE techniques. To this end, I will introduce Instance Space Analysis (ISA), which is an approach to identify features of SBSE problems that explain why a particular instance is difficult for an SBSE technique. ISA can be used to examine the diversity and quality of the benchmark datasets used by most researchers, and analyse the strengths and weaknesses of existing SBSE techniques. The instance space is constructed to reveal areas of hard and easy problems, and enables the strengths and weaknesses of the different SBSE techniques to be identified. I will present on how ISA enabled us to identify the strengths and weaknesses of SBSE techniques in two areas: Search-Based Software Testing and Automated Program Repair. Finally, I will end my talk with future directions of the objective assessment of SBSE techniques.
The performance of deep neural networks improves with more annotated data. The problem is that the budget for annotation is limited. One solution to this is active learning, where a model asks human to annotate data that it perceived as uncertain. A variety of recent methods have been proposed to apply active learning to deep networks but most of them are either designed specific for their target tasks or computationally inefficient for large networks. In this paper, we propose a novel active learning method that is simple but task-agnostic, and works efficiently with the deep networks. We attach a small parametric module, named “loss prediction module,” to a target network, and learn it to predict target losses of unlabeled inputs. Then, this module can suggest data that the target model is likely to produce a wrong prediction. This method is task-agnostic as networks are learned from a single loss regardless of target tasks. We rigorously validate our method through image classification, object detection, and human pose estimation, with the recent network architectures. The results demonstrate that our method consistently outperforms the previous methods over the tasks
The Power of Auto ML and How Does it WorkIvo Andreev
Automated ML is an approach to minimize the need of data science effort by enabling domain experts to build ML models without having deep knowledge of algorithms, mathematics or programming skills. The mechanism works by allowing end-users to simply provide data and the system automatically does the rest by determining approach to perform particular ML task. At first this may sound discouraging to those aiming to the “sexiest job of the 21st century” - the data scientists. However, Auto ML should be considered as democratization of ML, rather that automatic data science.
In this session we will talk about how Auto ML works, how is it implemented by Microsoft and how it could improve the productivity of even professional data scientists.
Predictive Model and Record Description with Segmented Sensitivity Analysis (...Greg Makowski
Describing a predictive data mining model can provide a competitive advantage for solving business problems with a model. The SSA approach can also provide reasons for the forecast for each record. This can help drive investigations into fields and interactions during a data mining project, as well as identifying "data drift" between the original training data, and the current scoring data. I am working on open source version of SSA, first in R.
Machine Learning Foundations for Professional ManagersAlbert Y. C. Chen
20180804@Taiwan AI Academy, Hsinchu
6 hour lecture for those new to machine learning, to grasps the concepts, advantages and limitations of various classical machine learning methods. More importantly, to learn the skills to break down large complicated AI projects into manageable pieces, where features and functionalities could be added incrementally and annotated data accumulated. Take home message: machine learning is always a delicate balance between model complexity M and number of data N so that the trained classifier generalizes well and does not overfit.
In this talk we explore how to build Machine Learning Systems that can that can learn "continuously" from their mistakes (feedback loop) and adapt to an evolving data distribution.
The youtube link to video of the talk is here:
https://www.youtube.com/watch?v=VtBvmrmMJaI
The Machine Learning Workflow with AzureIvo Andreev
Machine learning is not black magic but a discipline that involves data analysis, data science and of course – hard work. From searching patterns in data, applying algorithms to converting to usable predictions, you would need background and appropriate tools. In this session, we will go through major approaches to prepare data, build and deploy ML models in Azure (ML Studio, DataScience VM, Jupyter Notebook). Most importantly – based on some examples from the real world, we will provide you with a workflow of best practices.
Machine Learning 2 deep Learning: An IntroSi Krishan
Provides a brief introduction to machine learning, reasons for its popularity, a simple walk through example and then a need for deep learning and some of its characteristics. This is an updated version of an earlier presentation.
This presentation inludes step-by step tutorial by including the screen recordings to learn Rapid Miner.It also includes the step-step-step procedure to use the most interesting features -Turbo Prep and Auto Model.
DutchMLSchool 2022 - History and Developments in MLBigML, Inc
History and Present Developments in Machine Learning, by Tom Dietterich, Emeritus Professor of computer science at Oregon State University and Chief Scientist at BigML.
*Machine Learning School in The Netherlands 2022.
Foundations of Machine Learning - StampedeCon AI Summit 2017StampedeCon
This presentation will cover all aspects of modeling, from preparing data, training and evaluating the results. There will be descriptions of the mainline ML methods including, neural nets, SVM, boosting, bagging, trees, forests, and deep learning. common problems of overfitting and dimensionality will be covered with discussion of modeling best practices. Other topics will include field standardization, encoding categorical variables, feature creation and selection. It will be a soup-to-nuts overview of all the necessary procedures for building state-of-the art predictive models.
Azure Machine Learning and ML on PremisesIvo Andreev
Machine Learning finds patterns in large volumes of data and uses those patterns to perform predictive analysis.Microsoft offers Azure Machine Learning, while Amazon offers Amazon Machine Learning and Google offers the Google Prediction API - now depricated and replaced by Google ML engine based on TensorFlow. Software products such as MATLAB support traditional, non-cloud-based ML modeling.
الموعد الإثنين 03 يناير 2022
143
مبادرة
#تواصل_تطوير
المحاضرة ال 143 من المبادرة
المهندس / محمد الرافعي طرباي
نقيب المبرمجين بالدقهلية
بعنوان
"IT INDUSTRY"
How To Getting Into IT With Zero Experience
وذلك يوم الإثنين 03 يناير2022
السابعة مساء توقيت القاهرة
الثامنة مساء توقيت مكة المكرمة
و الحضور من تطبيق زووم
https://us02web.zoom.us/meeting/register/tZUpf-GsrD4jH9N9AxO39J013c1D4bqJNTcu
علما ان هناك بث مباشر للمحاضرة على القنوات الخاصة بجمعية المهندسين المصريين
ونأمل أن نوفق في تقديم ما ينفع المهندس ومهمة الهندسة في عالمنا العربي
والله الموفق
للتواصل مع إدارة المبادرة عبر قناة التليجرام
https://t.me/EEAKSA
ومتابعة المبادرة والبث المباشر عبر نوافذنا المختلفة
رابط اللينكدان والمكتبة الالكترونية
https://www.linkedin.com/company/eeaksa-egyptian-engineers-association/
رابط قناة التويتر
https://twitter.com/eeaksa
رابط قناة الفيسبوك
https://www.facebook.com/EEAKSA
رابط قناة اليوتيوب
https://www.youtube.com/user/EEAchannal
رابط التسجيل العام للمحاضرات
https://forms.gle/vVmw7L187tiATRPw9
ملحوظة : توجد شهادات حضور مجانية لمن يسجل فى رابط التقيم اخر المحاضرة
Machine learning and linear regression programmingSoumya Mukherjee
Overview of AI and ML
Terminology awareness
Applications in real world
Use cases within Nokia
Types of Learning
Regression
Classification
Clustering
Linear Regression Single Variable with python
How to transform and select variables/features when creating a predictive model using machine learning. To see the source code visit https://github.com/Davisy/Feature-Engineering-and-Feature-Selection
The world of search engine optimization (SEO) is buzzing with discussions after Google confirmed that around 2,500 leaked internal documents related to its Search feature are indeed authentic. The revelation has sparked significant concerns within the SEO community. The leaked documents were initially reported by SEO experts Rand Fishkin and Mike King, igniting widespread analysis and discourse. For More Info:- https://news.arihantwebtech.com/search-disrupted-googles-leaked-documents-rock-the-seo-world/
RMD24 | Retail media: hoe zet je dit in als je geen AH of Unilever bent? Heid...BBPMedia1
Grote partijen zijn al een tijdje onderweg met retail media. Ondertussen worden in dit domein ook de kansen zichtbaar voor andere spelers in de markt. Maar met die kansen ontstaan ook vragen: Zelf retail media worden of erop adverteren? In welke fase van de funnel past het en hoe integreer je het in een mediaplan? Wat is nu precies het verschil met marketplaces en Programmatic ads? In dit half uur beslechten we de dilemma's en krijg je antwoorden op wanneer het voor jou tijd is om de volgende stap te zetten.
RMD24 | Debunking the non-endemic revenue myth Marvin Vacquier Droop | First ...BBPMedia1
Marvin neemt je in deze presentatie mee in de voordelen van non-endemic advertising op retail media netwerken. Hij brengt ook de uitdagingen in beeld die de markt op dit moment heeft op het gebied van retail media voor niet-leveranciers.
Retail media wordt gezien als het nieuwe advertising-medium en ook mediabureaus richten massaal retail media-afdelingen op. Merken die niet in de betreffende winkel liggen staan ook nog niet in de rij om op de retail media netwerken te adverteren. Marvin belicht de uitdagingen die er zijn om echt aansluiting te vinden op die markt van non-endemic advertising.
Unveiling the Secrets How Does Generative AI Work.pdfSam H
At its core, generative artificial intelligence relies on the concept of generative models, which serve as engines that churn out entirely new data resembling their training data. It is like a sculptor who has studied so many forms found in nature and then uses this knowledge to create sculptures from his imagination that have never been seen before anywhere else. If taken to cyberspace, gans work almost the same way.
"𝑩𝑬𝑮𝑼𝑵 𝑾𝑰𝑻𝑯 𝑻𝑱 𝑰𝑺 𝑯𝑨𝑳𝑭 𝑫𝑶𝑵𝑬"
𝐓𝐉 𝐂𝐨𝐦𝐬 (𝐓𝐉 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬) is a professional event agency that includes experts in the event-organizing market in Vietnam, Korea, and ASEAN countries. We provide unlimited types of events from Music concerts, Fan meetings, and Culture festivals to Corporate events, Internal company events, Golf tournaments, MICE events, and Exhibitions.
𝐓𝐉 𝐂𝐨𝐦𝐬 provides unlimited package services including such as Event organizing, Event planning, Event production, Manpower, PR marketing, Design 2D/3D, VIP protocols, Interpreter agency, etc.
Sports events - Golf competitions/billiards competitions/company sports events: dynamic and challenging
⭐ 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐝 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬:
➢ 2024 BAEKHYUN [Lonsdaleite] IN HO CHI MINH
➢ SUPER JUNIOR-L.S.S. THE SHOW : Th3ee Guys in HO CHI MINH
➢FreenBecky 1st Fan Meeting in Vietnam
➢CHILDREN ART EXHIBITION 2024: BEYOND BARRIERS
➢ WOW K-Music Festival 2023
➢ Winner [CROSS] Tour in HCM
➢ Super Show 9 in HCM with Super Junior
➢ HCMC - Gyeongsangbuk-do Culture and Tourism Festival
➢ Korean Vietnam Partnership - Fair with LG
➢ Korean President visits Samsung Electronics R&D Center
➢ Vietnam Food Expo with Lotte Wellfood
"𝐄𝐯𝐞𝐫𝐲 𝐞𝐯𝐞𝐧𝐭 𝐢𝐬 𝐚 𝐬𝐭𝐨𝐫𝐲, 𝐚 𝐬𝐩𝐞𝐜𝐢𝐚𝐥 𝐣𝐨𝐮𝐫𝐧𝐞𝐲. 𝐖𝐞 𝐚𝐥𝐰𝐚𝐲𝐬 𝐛𝐞𝐥𝐢𝐞𝐯𝐞 𝐭𝐡𝐚𝐭 𝐬𝐡𝐨𝐫𝐭𝐥𝐲 𝐲𝐨𝐮 𝐰𝐢𝐥𝐥 𝐛𝐞 𝐚 𝐩𝐚𝐫𝐭 𝐨𝐟 𝐨𝐮𝐫 𝐬𝐭𝐨𝐫𝐢𝐞𝐬."
Discover the innovative and creative projects that highlight my journey throu...dylandmeas
Discover the innovative and creative projects that highlight my journey through Full Sail University. Below, you’ll find a collection of my work showcasing my skills and expertise in digital marketing, event planning, and media production.
Premium MEAN Stack Development Solutions for Modern BusinessesSynapseIndia
Stay ahead of the curve with our premium MEAN Stack Development Solutions. Our expert developers utilize MongoDB, Express.js, AngularJS, and Node.js to create modern and responsive web applications. Trust us for cutting-edge solutions that drive your business growth and success.
Know more: https://www.synapseindia.com/technology/mean-stack-development-company.html
Cracking the Workplace Discipline Code Main.pptxWorkforce Group
Cultivating and maintaining discipline within teams is a critical differentiator for successful organisations.
Forward-thinking leaders and business managers understand the impact that discipline has on organisational success. A disciplined workforce operates with clarity, focus, and a shared understanding of expectations, ultimately driving better results, optimising productivity, and facilitating seamless collaboration.
Although discipline is not a one-size-fits-all approach, it can help create a work environment that encourages personal growth and accountability rather than solely relying on punitive measures.
In this deck, you will learn the significance of workplace discipline for organisational success. You’ll also learn
• Four (4) workplace discipline methods you should consider
• The best and most practical approach to implementing workplace discipline.
• Three (3) key tips to maintain a disciplined workplace.
Improving profitability for small businessBen Wann
In this comprehensive presentation, we will explore strategies and practical tips for enhancing profitability in small businesses. Tailored to meet the unique challenges faced by small enterprises, this session covers various aspects that directly impact the bottom line. Attendees will learn how to optimize operational efficiency, manage expenses, and increase revenue through innovative marketing and customer engagement techniques.
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Attending a job Interview for B1 and B2 Englsih learnersErika906060
It is a sample of an interview for a business english class for pre-intermediate and intermediate english students with emphasis on the speking ability.
What is the TDS Return Filing Due Date for FY 2024-25.pdfseoforlegalpillers
It is crucial for the taxpayers to understand about the TDS Return Filing Due Date, so that they can fulfill your TDS obligations efficiently. Taxpayers can avoid penalties by sticking to the deadlines and by accurate filing of TDS. Timely filing of TDS will make sure about the availability of tax credits. You can also seek the professional guidance of experts like Legal Pillers for timely filing of the TDS Return.
Memorandum Of Association Constitution of Company.pptseri bangash
www.seribangash.com
A Memorandum of Association (MOA) is a legal document that outlines the fundamental principles and objectives upon which a company operates. It serves as the company's charter or constitution and defines the scope of its activities. Here's a detailed note on the MOA:
Contents of Memorandum of Association:
Name Clause: This clause states the name of the company, which should end with words like "Limited" or "Ltd." for a public limited company and "Private Limited" or "Pvt. Ltd." for a private limited company.
https://seribangash.com/article-of-association-is-legal-doc-of-company/
Registered Office Clause: It specifies the location where the company's registered office is situated. This office is where all official communications and notices are sent.
Objective Clause: This clause delineates the main objectives for which the company is formed. It's important to define these objectives clearly, as the company cannot undertake activities beyond those mentioned in this clause.
www.seribangash.com
Liability Clause: It outlines the extent of liability of the company's members. In the case of companies limited by shares, the liability of members is limited to the amount unpaid on their shares. For companies limited by guarantee, members' liability is limited to the amount they undertake to contribute if the company is wound up.
https://seribangash.com/promotors-is-person-conceived-formation-company/
Capital Clause: This clause specifies the authorized capital of the company, i.e., the maximum amount of share capital the company is authorized to issue. It also mentions the division of this capital into shares and their respective nominal value.
Association Clause: It simply states that the subscribers wish to form a company and agree to become members of it, in accordance with the terms of the MOA.
Importance of Memorandum of Association:
Legal Requirement: The MOA is a legal requirement for the formation of a company. It must be filed with the Registrar of Companies during the incorporation process.
Constitutional Document: It serves as the company's constitutional document, defining its scope, powers, and limitations.
Protection of Members: It protects the interests of the company's members by clearly defining the objectives and limiting their liability.
External Communication: It provides clarity to external parties, such as investors, creditors, and regulatory authorities, regarding the company's objectives and powers.
https://seribangash.com/difference-public-and-private-company-law/
Binding Authority: The company and its members are bound by the provisions of the MOA. Any action taken beyond its scope may be considered ultra vires (beyond the powers) of the company and therefore void.
Amendment of MOA:
While the MOA lays down the company's fundamental principles, it is not entirely immutable. It can be amended, but only under specific circumstances and in compliance with legal procedures. Amendments typically require shareholder
[Note: This is a partial preview. To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
Sustainability has become an increasingly critical topic as the world recognizes the need to protect our planet and its resources for future generations. Sustainability means meeting our current needs without compromising the ability of future generations to meet theirs. It involves long-term planning and consideration of the consequences of our actions. The goal is to create strategies that ensure the long-term viability of People, Planet, and Profit.
Leading companies such as Nike, Toyota, and Siemens are prioritizing sustainable innovation in their business models, setting an example for others to follow. In this Sustainability training presentation, you will learn key concepts, principles, and practices of sustainability applicable across industries. This training aims to create awareness and educate employees, senior executives, consultants, and other key stakeholders, including investors, policymakers, and supply chain partners, on the importance and implementation of sustainability.
LEARNING OBJECTIVES
1. Develop a comprehensive understanding of the fundamental principles and concepts that form the foundation of sustainability within corporate environments.
2. Explore the sustainability implementation model, focusing on effective measures and reporting strategies to track and communicate sustainability efforts.
3. Identify and define best practices and critical success factors essential for achieving sustainability goals within organizations.
CONTENTS
1. Introduction and Key Concepts of Sustainability
2. Principles and Practices of Sustainability
3. Measures and Reporting in Sustainability
4. Sustainability Implementation & Best Practices
To download the complete presentation, visit: https://www.oeconsulting.com.sg/training-presentations
Sustainability: Balancing the Environment, Equity & Economy
Kaggle Gold Medal Case Study
1. Kaggle Gold Medal: Case Study
My part of our team’s 9th place solution
Alon Bochman
October 8th, 2019
2. Summary: Credit risk model wins a Kaggle gold medal
Home Credit (HC) is a global
consumer-lender focused on the
unbanked and underbanked
In late 2018, HC challenged the Kaggle
community to create innovative credit
scoring techniques
HC provided data on ~350,000
applicants covering applications, credit
bureau files, credit card balances, loan
payment history etc.
Models were ranked purely on
predictive power as measured by
AUROC on a holdout (test) set of
applicants
Background
Our team received a gold medal,
ranked 9th out of 7,198 teams
Ahead of most Kaggle grandmasters
Results
Our Process
Data Exploration
Feature
Engineering
Modeling Ensembling
Challenge • Data provided from 7
sources, not ready-
to-model
• Hundreds of
quantitative and text
features provided
• Uneven data
quality: Many
missing, miscoded
values
• Text features with
high cardinality
• Non-linear
relationships
• Highly unbalanced data:
Only 8% of applicants in
training set
experiencing default
• Test and train set came
from different
populations (time split)
• No single model
strong enough to win
a gold medal
Solution • Leverage data to infer
structure
• Aggregate by
common IDs + using
matching algorithm
• Rapid exploration to
identify important
features
• EDA, domain
expertise and
custom code to
identify feature
interactions
• Public Q&A with
competition host
to understand
underlying process,
dataset
construction
• Leverage non-linear
modeling techniques:
xgboost, lightgbm, knn,
neural networking and
others
• Innovative model
composition with
misclassification-boost
• Adversarial validation
with stratified k-fold CV
• Combine models in a
stacked ensemble
similar to a neural-
network architecture
• Final ensemble
consisted of ~300
models in a 5-layer
stack
3. Data exploration
- 7 datasets with nested
many-to-one relationships.
For example, can have:
- Many previous
applications from the
applicant and
- Many installment
payments per previous
application
- Key issue
- How to aggregate nested
data into model-able
features
- Our solution
- Many different
aggregations – see feature
4. Feature engineering
Basic techniques
• Numeric
• Scaling*: Min/Max, z-score,
Exponential, PctRank, Winsorize
• Round precision (how many zeros)
• Binning
• Categorical
• Label, frequency, mean encoding
• One-hot encoding*
• Date
• Dates provided as integers (days
since X)
• Missings
• Flag hidden NaNs
• Count by row, % vs peer group
• Impute with median, mean, model
• Aggregation
• Standard stats
(min/median/sd/kurtosis, etc.)
Created ~3,000 features using domain knowledge, EDA and systematic search. About 80% of total effort was
spent here
Notes
* For non-tree models
Advanced techniques
• Numeric
• Round precision (how many zeros)
• Aggregation
• Stats vs peer group
• Triangulation
• Application vs. bureau
• Current vs. previous
• Bureau 1 vs. 2, etc.
• Interaction search
• Using Spearman rank correlation:
fast and compatible with AUC goal
• Using tree-based models, built-into
CatBoost
• 2-way and 3-way
• Distance
• KNN with PCA (next slide)
Feature selection
• Stepwise selection
• Linear models:
• Lasso regression (at
ensemble level)
• Tree models:
• Split, gain importance (not
great for high-cardinality
features)
• Permutation importance
• Permuting the features (one
feature at a time, best but
expensive)
• Permuting the target (~30
times)
5. Distance features with KNN
The KNN model contributed many useful features at the base and ensemble levels
Raw Features Scale Reduce (Optional)
Fit Nearest
Neighbor Model
Generate
Meta-features
• Applied to many
feature-sets at parent
and child levels
• Applied at ensemble
level (stacking)
• Treat NULLs
• Numerics: Z-score or 0-1
scaling
• Categoricals: mean-
encode (out of fold)
• PCA
• Non-negative Matrix
Factorization (NMF)
• Required if number of
features > about 5
• Produce vector of
nearest neighbors and
distances for each
observation
• Multiple distance
measures: Euclidian,
Bray-Curtis, Manhattan
• CV, fit out-of-fold
• % of closest K observations in each
class (default or non-default)
where K is [5, 10, 100, 500, 2000,
10,000]
• Largest sequence of consecutive
neighbors of each class within K
closest observations
• Distance to closest neighbor of
each class (default and non-
default)
• Mean distance to neighbors of
each class in closest K
observations
• And many others
Example
Neighbor Distance Target
1 0.1 0
2 0.2 0
3 0.4 1
…
Feature Value
Distance to nearest target=0 0.1
Distance to nearest target=1 0.4
Mean distance to target=0 in top 3
neighbors
0.15
….
KNN output Meta-features
Advantages of this approach
• Non-parametric
• Non-linear
• Complementary: Can identify complex patterns
other models can’t
• Relatively inexpensive, computationally
6. Modeling
Conducted 375 experiments using 11 model types. GBMs were most effective for this problem
Models built
• GLM
• Logistic
• Distance
• KNN
• SVM*
• Deep Learning
• Keras /
TensorFlow
• Tree
• RandomForest
• ExtraTrees*
• XGBoost
• LightGBM
• CatBoost
• AutoML
• H2O Driverless AI
• TPOT*
• Others*
Learnings
• Gradient boosting machines (GBMs) were most
effective for this type of problem due to
• Structured data of moderate size (<1m
observations)
• Nonlinear, complex relationships
• Useful for feature selection
• All model types contributed to winning solution by
creating diversity (see footnote for exceptions)
• GBMs and NNs benefitted from
• Bayesian parameter optimization
• Early stopping (iterations / epochs)
• Bagging (5-10x with random seeds)
• Despite vendor claims, AutoML solutions were
unimpressive at the time (June-August 2018)
• H2OAI had moderate standalone performance,
but added value to the ensemble
GBMs
Note
* Did not contribute significantly to winning solution
7. Modeling many-to-one relationships
A key solution was to model at the most granular (child) level and aggregate the predictions to the parent level.
This solution worked for nested relationships as well, such as bureau_balance to bureau to application
App ID
Prev App
ID
Prev App
Features…
App1 Prev1,
Prev2
App2 Prev3
Parent level: application.csv
Child level: previous_application.csv
Applicant 1
had two
previous
application
s: P1 and
P2
App
ID
Prev
ID
Prev App
Features… Target Pred
App1 Prev1 0 0.1
App1 Prev2 0 0.2
App2 Prev3 1 0.9
X Y
• Merge in target variable from
parent level (application) using
SQL JOIN
• Fit model at the child level
(previous_application)
Predict at child level
App
ID
Mean
Pred
Min
Pred
Other
Stats…
App1 0.15 0.1
App2 0.9 0.9
• Group predictions by parent
level (App ID)
• Calculate statistics such as mean,
median, max, stdev, skewness,
kurtosis, 5th percentile, first, last,
trend, etc.
Aggregate to parent level
YY
Stats on grouped by App ID
8. Ensembling
Ensembling and stacking are critical to most Kaggle contests. The key is to identify uncorrelated but strong
classifiers
Classifier Prediction Accuracy
A 1111111100 80%
B 1111111100 80%
C 1011111100 70%
Majority 1111111100 80%
Ground Truth 1111111111
Classifier Prediction Accuracy
D 1111111100 80%
E 0111011101 70%
F 1000101111 60%
Majority 1111111101 90%
Correlated: No benefit Less correlated: High
benefit
Source: MLWave.com
“Majority vote” is a simple
ensemble. We can get
fancier with:
• Weighted average
• Rank average
• L2 model (stack)
• And so on
9. Stacking
Our solution consisted of ~300 models in a 5 layer stack
Feature-set
X1
7 raw
dataset
s
Feature-set
X2
Feature-set
X22
Model
L1.1
Model
L1.2
Model
L1.11
95 L1
Prediction
s
Model
L2.1
Model
L2.2
Model
L2.5
Model L5
Predictions submitted to Kaggle
L2
Prediction
s
~3,000
features in
total
• Stratified 5-fold cross
validation
• Out-of-fold predictions
gathered as L1 dataset
• Bayesian and random
parameter optimization
• Feature engineering and
selection applied iteratively
At higher levels of the
stack, we used
• Fewer models
• Simpler models,
and/or
• Stronger
regularization
Data
Model
Continued adding
levels until local CV
score stopped
improving
meaningfully
10. Cross-model boosting
We were able to extract additional signal by stacking two models together using an algorithm similar to gradient
boosting, with a focus on misclassification errors. This approach is novel as far as we know
FPR
TPR
Motivation
AUC
Worst
false
positives
Worst
false
negative
s
• Not all errors are equally important to
fix. Some are more “expensive” to the
AUC
• We would like to create / select feature
(sets) that add to our signal
Execution
Procedure
1. Fit classifier A on feature set X1. Get
out-of-fold predictions
2. Tag worst 10% false positives as
class=1, rest as class=0
3. Fit classifier B on feature set X2 to
predict this class. Call this the FP
model
4. Similarly, create the FN model
5. Fit classifier C on (OOF) predictions
from models A, FP and FN and their
interactions (several variations
possible)
Rationale
• Classifier B picks X2 features most
complementary to X1
• Classifier C fixes A’s worst errors
• But: watch out for overfitting
Worst 10%
false positives:
“Safe-looking
borrowers
that
defaulted”
Worst 10%
false
negatives:
“Risky-looking
borrowers
that did not
default”
Model A classification error (Y −
Y)
11. Team coordination
Problems
• 8 teammates in 8 timezones. 2-3 awake & online at any given
time
• Varying skill levels, experience with Kaggle, strengths,
availability
• Everyone is a volunteer, not like at work
• Limited number of submissions allowed – must be
coordinated
• Infinite work, limited time – just like in real life
Our solution
• Slack!
• Theme-specific channels helped focus the discussion
• Shared validation scheme: stratified 5-fold, shared random
seed. Produces comparable results, OOFs for stacking
• Everyone announces their work direction and progress
• OOFs, stacking datasets posted for full team to use
• Much room for improvement, here and elsewhere
Feature
engineering
Reusable code
(poor man’s
git)
Out-of-fold
predictions for
stacking
Intro for new
team
members
Coordinating
submissions
12. What didn’t work, what we missed
Despite our gold-medal finish, many of our experiments did not work, and we learned a great deal from other top
teams
What didn’t work
• Auto-regressive approach on timeseries
• Binning the timeseries
• Symbolic regression w/genetic feature
generation (DEAP)
• AUC oracle probing
• NMF factorization
• T-SNE projection
• Learning rate decay
• Different number of folds (3, 10, 15)
• Adversarial validation
What we missed
• Interest rate imputation
• Better feature selection: 3rd place solution
used just 150 features
• DAE + NN (component of 1st place solution)
• Encoding payment history as an “image” and
running it through a CNN
• Same borrowers with different IDs
• And many more…
13. THANK YOU!
Special thanks to my fantastic team: Michael Penrose, Corey Levinson, Sai Suchith
Mahajan, Misha Lisovyi, Tom Aindow and Zipp!
15. Data Exploration: Selected Findings
Finding Implication
Only 8% of borrowers defaulted in training set Stratified k-fold validation scheme
Extreme outliers in time-based variables, e.g. 1000-year
employment history
• They default less often (5.4% vs. 8.7%)
• Similar effect with some income outliers, e.g. $10M
annual income
Encode outliers as NULLs
Encode outlier flags for algos that aren’t NULL-friendly
(eg. GLM, Scikit’s RandomForest)
Up to 70% missing data in certain variables Create features on missing data (count, groupby)
Certain categorical variables with high cardinality Frequency encoding
Mean encoding (out-of-fold)
Text-processing to create lower-cardinality groups
16. Selected high-value (top 1%) engineered features
Feature Why it was useful
Credit requested / annual loan payment Loan duration. Longer loans are riskier, all else equal
Variance of (debt / credit) reported by credit
bureaus
• Debt / credit is a proxy for borrowing flexibility, i.e. financial
capacity
• When bureaus paint a consistent picture, the applicant is
better-known and safer
Financial product (card, revolving loan, line of
credit, etc.) applied-for in most recent
application
More predictive than an aggregation of full application history
Unweighted mean of all mean encodings by row Mean reduced variance of mean-encodings
Minimum of all mean encodings by row Added sensitivity for borderline applicants, similar to worst-
case-scenario
(Proposed purchase price / credit requested)
ranked within groups defined by whether a work
phone was provided
• Ratio normalized for different income levels. NULLs also
predictive
• Ranking corrected for non-linearity
• Grouping made comparison more fair. Within-group rank
more predictive than across-group
Simpler
More
Complex
17. Out of fold predictions
Our 5-fold validation scheme allowed us to create out-of-fold predictions for each model
1. Split train set into 5
folds (stratified)
2. Train model L1.1 on
folds 2 through 5.
Predict on fold 1.
These are out-of-fold
predictions for fold 1.
Save model weights
3. Repeat #2 to create
out-of-fold predictions
for all folds
4. Average trained
models for all folds to
predict the test set
Learn
Learn
Learn
Learn
Predict
Learn
Learn
Learn
Predict
Learn
Learn
Learn
Predict
Learn
Learn
Learn
Predict
Learn
Learn
Learn
Predict
Learn
Learn
Learn
Learn
Train
Set
Test
Set
Predict Predict Predict Predict Predict
Predict
Predict
Predict
Predict
Predict
Predict
Mean
Meta-featureFold 1 Fold 2 Fold 3 Fold 4 Fold 5
18. Stacking
We concatenate L1 predictions as columns into the L1 dataset. Then, we can fit an L2 stacking model on top of it.
The process repeats for higher levels
ID TARGET Logistic Random
Forest
LightGBM Neural
Network
Others…
Train
Set
Test
Set
Meta-features
prepared as in
previous slide
Level 2
Model
(Logistic)
L2
Meta-
Feature
Test
preds
From raw data Predictions from L1
Models
Submitted to Kaggle
Editor's Notes
Why Kaggle?
Largest DS community globally with >120k ranked competitors
Great place to learn: Competition and cooperation
Time and resource constraints just like with real project work
Level playing field + objective performance evaluation
Focus on what works in practice vs. theory
Can evaluate competing algorithms, pipelines, scientists
Competition pushes the envelope
How much signal can we possibly squeeze from the data?
Can sometimes advance the state of the art
