This document contains information about various machine learning algorithms including linear regression, decision trees, naive bayes, logistic regression, and neural networks. It also contains a table with details about superheroes such as their name, birth year, abilities, and alignment.
Scaling Ride-Hailing with Machine Learning on MLflowDatabricks
"GOJEK, the Southeast Asian super-app, has seen an explosive growth in both users and data over the past three years. Today the technology startup uses big data powered machine learning to inform decision-making in its ride-hailing, lifestyle, logistics, food delivery, and payment products. From selecting the right driver to dispatch, to dynamically setting prices, to serving food recommendations, to forecasting real-world events. Hundreds of millions of orders per month, across 18 products, are all driven by machine learning.
Building production grade machine learning systems at GOJEK wasn't always easy. Data processing and machine learning pipelines were brittle, long running, and had low reproducibility. Models and experiments were difficult to track, which led to downstream problems in production during serving and model evaluation. In this talk we will cover these and other challenges that we faced while trying to scale end-to-end machine learning systems at GOJEK. We will then introduce MLflow and explore the key features that make it useful as part of an ML platform. Finally, we will show how introducing MLflow into the ML life cycle has helped to solve many of the problems we faced while scaling machine learning at GOJEK.
"
Automated Hyperparameter Tuning, Scaling and TrackingDatabricks
Automated Machine Learning (AutoML) has received significant interest recently. We believe that the right automation would bring significant value and dramatically shorten time-to-value for data science teams. Databricks is automating the Data Science and Machine Learning process through a combination of product offerings, partnerships, and custom solutions. This talk will focus on how Databricks can help automate hyperparameter tuning.
For both traditional Machine Learning and modern Deep Learning, tuning hyperparameters can dramatically increase model performance and improve training times. However, tuning can be a complex and expensive process. In this talk, we'll start with a brief survey of the most popular techniques for hyperparameter tuning (e.g., grid search, random search, and Bayesian optimization). We will then discuss open source tools that implement each of these techniques, helping to automate the search over hyperparameters.
Finally, we will discuss and demo improvements we built for these tools in Databricks, including integration with MLflow:
Apache PySpark MLlib integration with MLflow for automatically tracking tuning
Hyperopt integration with Apache Spark to distribute tuning and with MLflow for automatic tracking
Recording and notebooks will be provided after the webinar so that you can practice at your own pace.
Presenters
Joseph Bradley, Software Engineer, Databricks
Joseph Bradley is a Software Engineer and Apache Spark PMC member working on Machine Learning at Databricks. Previously, he was a postdoc at UC Berkeley after receiving his Ph.D. in Machine Learning from Carnegie Mellon in 2013.
Yifan Cao, Senior Product Manager, Databricks
Yifan Cao is a Senior Product Manager at Databricks. His product area spans ML/DL algorithms and Databricks Runtime for Machine Learning. Prior to Databricks, Yifan worked on two Machine Learning products, applying NLP to find metadata and applying machine learning to predict equipment failures. He helped build the products from ground up to multi-million dollars in ARR. Yifan started his career as a researcher in quantum computing. Yifan received his B.S in UC Berkeley and Master from MIT.
Scaling Ride-Hailing with Machine Learning on MLflowDatabricks
"GOJEK, the Southeast Asian super-app, has seen an explosive growth in both users and data over the past three years. Today the technology startup uses big data powered machine learning to inform decision-making in its ride-hailing, lifestyle, logistics, food delivery, and payment products. From selecting the right driver to dispatch, to dynamically setting prices, to serving food recommendations, to forecasting real-world events. Hundreds of millions of orders per month, across 18 products, are all driven by machine learning.
Building production grade machine learning systems at GOJEK wasn't always easy. Data processing and machine learning pipelines were brittle, long running, and had low reproducibility. Models and experiments were difficult to track, which led to downstream problems in production during serving and model evaluation. In this talk we will cover these and other challenges that we faced while trying to scale end-to-end machine learning systems at GOJEK. We will then introduce MLflow and explore the key features that make it useful as part of an ML platform. Finally, we will show how introducing MLflow into the ML life cycle has helped to solve many of the problems we faced while scaling machine learning at GOJEK.
"
Automated Hyperparameter Tuning, Scaling and TrackingDatabricks
Automated Machine Learning (AutoML) has received significant interest recently. We believe that the right automation would bring significant value and dramatically shorten time-to-value for data science teams. Databricks is automating the Data Science and Machine Learning process through a combination of product offerings, partnerships, and custom solutions. This talk will focus on how Databricks can help automate hyperparameter tuning.
For both traditional Machine Learning and modern Deep Learning, tuning hyperparameters can dramatically increase model performance and improve training times. However, tuning can be a complex and expensive process. In this talk, we'll start with a brief survey of the most popular techniques for hyperparameter tuning (e.g., grid search, random search, and Bayesian optimization). We will then discuss open source tools that implement each of these techniques, helping to automate the search over hyperparameters.
Finally, we will discuss and demo improvements we built for these tools in Databricks, including integration with MLflow:
Apache PySpark MLlib integration with MLflow for automatically tracking tuning
Hyperopt integration with Apache Spark to distribute tuning and with MLflow for automatic tracking
Recording and notebooks will be provided after the webinar so that you can practice at your own pace.
Presenters
Joseph Bradley, Software Engineer, Databricks
Joseph Bradley is a Software Engineer and Apache Spark PMC member working on Machine Learning at Databricks. Previously, he was a postdoc at UC Berkeley after receiving his Ph.D. in Machine Learning from Carnegie Mellon in 2013.
Yifan Cao, Senior Product Manager, Databricks
Yifan Cao is a Senior Product Manager at Databricks. His product area spans ML/DL algorithms and Databricks Runtime for Machine Learning. Prior to Databricks, Yifan worked on two Machine Learning products, applying NLP to find metadata and applying machine learning to predict equipment failures. He helped build the products from ground up to multi-million dollars in ARR. Yifan started his career as a researcher in quantum computing. Yifan received his B.S in UC Berkeley and Master from MIT.
How to win data science competitions with Deep LearningSri Ambati
Note: Please download the slides first, otherwise some links won't work!
How to win kaggle style data science competitions and influence decisions with R, Deep Learning and H2O's fast algorithms.
We take a few public and kaggle datasets and model to win competitions on accuracy and scoring speed.
- 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
Machine Learning is all the rage today with many different options and paradigms. This session will walk through the basics of Machine Learning and show how to get started with the open source Spark ML framework. Through Scala code examples you will learn how to build and deploy learning systems like recommendation engines.
(Video and code at https://fsharpforfunandprofit.com/pipeline/)
Passing data through a pipeline of transformations is an alternative approach to classic OOP. The LINQ methods in .NET are designed around this, but the pipeline approach can be used for so much more than manipulating collections.
In this talk, I'll look at pipeline-oriented programming and how it relates to functional programming, the open-closed principle, unit testing, the onion architecture, and more. I'll finish up by showing how you can build a complete web app using only this approach.
My JSConf.eu talk about next-gen JavaScript metaprogramming features, starting with ES5's new Object APIs and then focusing on the forthcoming Proxy object, approved for the next ECMA-262 Edition. This is beautiful work from Tom Van Cutsem and Mark Miller, with Andreas Gal helping on the implementation front -- proxies are already shipping in Firefox 4 betas.
A presentation on how to avoid becoming a "single point of failure expert" - a person that monopolizes the knowledge about a system to a point when she is the only one who can fix it.
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.
How to win data science competitions with Deep LearningSri Ambati
Note: Please download the slides first, otherwise some links won't work!
How to win kaggle style data science competitions and influence decisions with R, Deep Learning and H2O's fast algorithms.
We take a few public and kaggle datasets and model to win competitions on accuracy and scoring speed.
- 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
Machine Learning is all the rage today with many different options and paradigms. This session will walk through the basics of Machine Learning and show how to get started with the open source Spark ML framework. Through Scala code examples you will learn how to build and deploy learning systems like recommendation engines.
(Video and code at https://fsharpforfunandprofit.com/pipeline/)
Passing data through a pipeline of transformations is an alternative approach to classic OOP. The LINQ methods in .NET are designed around this, but the pipeline approach can be used for so much more than manipulating collections.
In this talk, I'll look at pipeline-oriented programming and how it relates to functional programming, the open-closed principle, unit testing, the onion architecture, and more. I'll finish up by showing how you can build a complete web app using only this approach.
My JSConf.eu talk about next-gen JavaScript metaprogramming features, starting with ES5's new Object APIs and then focusing on the forthcoming Proxy object, approved for the next ECMA-262 Edition. This is beautiful work from Tom Van Cutsem and Mark Miller, with Andreas Gal helping on the implementation front -- proxies are already shipping in Firefox 4 betas.
A presentation on how to avoid becoming a "single point of failure expert" - a person that monopolizes the knowledge about a system to a point when she is the only one who can fix it.
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.
As a long time C# developer, I started with Python as a second language for ML purposes. Starting in Python is easy, doing engineering grade python turned out to be a lot harder, so these are 10 things I learned along the way to writing production code in Python.
AI and Ethics - We are the guardians of our futureTess Ferrandez
We're in charge, as data scientist and software engineers, of software that where mistakes have a very high price. It's time to look at the impact we have, how our software will be used and how to avoid creating unfair, biased and dangerous software
Updated version of the Practical Guide to Deep Learning with some actual projects, and our process of working through deep learning projects - less end to end, more joining several models together
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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.
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.
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.
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
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.
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.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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.
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.
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
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.
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.
38. ID First Name Last Name Birth Year Height Birthplace Identity is secret Can fly Alignment Wears cape
7435 Bruce Wayne 1969* 6'2'' Gotham Y 3 anti-villain black
0958 Ororo Munroe --1979-- 5'11'' Manhattan NA 9 Good long
9471 Diana Trevor 1618 5'8'' Paradise Island Y Jet truth rarely
9483 Janet Van Dyne 19.42 5'4'' Cresskill tiny Good not really
0696 Peter Parker 1111983 5'10'' Queens Y fall right never
5531 Harleen Quinzell 1981 5'2'' Gotham Y - evil no
4734 Erik Lehnsherr 1-9-3-2 6'0'' Hamburg NA lev. mutants Absolutely
7757 Natasha Romanova 1983 5'7'' St. Petersburg NA jet depends No way
0323 Jean Grey "1977" 5'6'' Annandale No good Mostly not
3990 Clark Kent "1954" 6'4'' Krypton Y 12 truth always
3057 Victor Von Doom "1943" 6'2'' Latveria Missing 1 Bad yes
0573 Stephen Strange 1968 6'2'' Philadelphia not light Y
7452 Thor Odinson 2287 BC 6'6'' Norway 10 Good Of course
1437 Selina Kyle 1998 5'7'' Gotham Y NA Neutral It clashes
1883 Raven Darkolme ..1911.. 5'10'' unkown Y no mostly bad not really
5830 Kara Zor-el 1961 5'7'' Krypton Y fast G Yes
39. ID First Name Last Name Birth Year Height Birthplace Identity is secret Can fly Alignment Wears cape
7435 Bruce Wayne 1969 6'2'' Gotham Y 3 anti-villain black
0958 Ororo Munroe 1979 5'11'' Manhattan NA 9 Good long
9471 Diana Trevor 1618 5'8'' Paradise Island Y Jet truth rarely
9483 Janet Van Dyne 1942 5'4'' Cresskill tiny Good not really
0696 Peter Parker 1983 5'10'' Queens Y fall right never
5531 Harleen Quinzell 1981 5'2'' Gotham Y - evil no
4734 Erik Lehnsherr 1932 6'0'' Hamburg NA lev. mutants Absolutely
7757 Natasha Romanova 1983 5'7'' St. Petersburg NA jet depends No way
0323 Jean Grey 1977 5'6'' Annandale No good Mostly not
3990 Clark Kent 1954 6'4'' Krypton Y 12 truth always
3057 Victor Von Doom 1943 6'2'' Latveria Missing 1 Bad yes
0573 Stephen Strange 1968 6'2'' Philadelphia not light Y
7452 Thor Odinson -2287 6'6'' Norway 10 Good Of course
1437 Selina Kyle 1998 5'7'' Gotham Y NA Neutral It clashes
1883 Raven Darkolme 1911 5'10'' unkown Y no mostly bad not really
5830 Kara Zor-el 1961 5'7'' Krypton Y fast G Yes
40. ID First Name Last Name Birth Year Height Birthplace Identity is secret Can fly Alignment Wears cape
7435 Bruce Wayne 1969 74 Gotham Y 3 anti-villain black
0958 Ororo Munroe 1979 71 Manhattan NA 9 Good long
9471 Diana Trevor 1618 68 Paradise Island Y Jet truth rarely
9483 Janet Van Dyne 1942 64 Cresskill tiny Good not really
0696 Peter Parker 1983 70 Queens Y fall right never
5531 Harleen Quinzell 1981 62 Gotham Y - evil no
4734 Erik Lehnsherr 1932 72 Hamburg NA lev. mutants Absolutely
7757 Natasha Romanova 1983 67 St. Petersburg NA jet depends No way
0323 Jean Grey 1977 66 Annandale No good Mostly not
3990 Clark Kent 1954 76 Krypton Y 12 truth always
3057 Victor Von Doom 1943 74 Latveria Missing 1 Bad yes
0573 Stephen Strange 1968 74 Philadelphia not light Y
7452 Thor Odinson -2287 78 Norway 10 Good Of course
1437 Selina Kyle 1998 67 Gotham Y NA Neutral It clashes
1883 Raven Darkolme 1911 70 unkown Y no mostly bad not really
5830 Kara Zor-el 1961 67 Krypton Y fast G Yes
41. ID First Name Last Name Birth Year Height Birthplace Identity is secret Can fly Alignment Wears cape
7435 Bruce Wayne 1969 74 Gotham Y 3 anti-villain black
0958 Ororo Munroe 1979 71 Manhattan N 9 Good long
9471 Diana Trevor 1618 68 Paradise Island Y Jet truth rarely
9483 Janet Van Dyne 1942 64 Cresskill N tiny Good not really
0696 Peter Parker 1983 70 Queens Y fall right never
5531 Harleen Quinzell 1981 62 Gotham Y - evil no
4734 Erik Lehnsherr 1932 72 Hamburg N lev. mutants Absolutely
7757 Natasha Romanova 1983 67 St. Petersburg N jet depends No way
0323 Jean Grey 1977 66 Annandale N No good Mostly not
3990 Clark Kent 1954 76 Krypton Y 12 truth always
3057 Victor Von Doom 1943 74 Latveria N 1 Bad yes
0573 Stephen Strange 1968 74 Philadelphia N not light Y
7452 Thor Odinson -2287 78 Norway N 10 Good Of course
1437 Selina Kyle 1998 67 Gotham Y NA Neutral It clashes
1883 Raven Darkolme 1911 70 unkown Y no mostly bad not really
5830 Kara Zor-el 1961 67 Krypton Y fast G Yes
42. ID First Name Last Name Birth Year Height Birthplace Identity is secret Can fly Alignment Wears cape
7435 Bruce Wayne 1969 74 Gotham Y N anti-villain black
0958 Ororo Munroe 1979 71 Manhattan N Y Good long
9471 Diana Trevor 1618 68 Paradise Island Y N truth rarely
9483 Janet Van Dyne 1942 64 Cresskill N N Good not really
0696 Peter Parker 1983 70 Queens Y N right never
5531 Harleen Quinzell 1981 62 Gotham Y N evil no
4734 Erik Lehnsherr 1932 72 Hamburg N N mutants Absolutely
7757 Natasha Romanova 1983 67 St. Petersburg N N depends No way
0323 Jean Grey 1977 66 Annandale N N good Mostly not
3990 Clark Kent 1954 76 Krypton Y Y truth always
3057 Victor Von Doom 1943 74 Latveria N N Bad yes
0573 Stephen Strange 1968 74 Philadelphia N N light Y
7452 Thor Odinson -2287 78 Norway N Y Good Of course
1437 Selina Kyle 1998 67 Gotham Y N Neutral It clashes
1883 Raven Darkolme 1911 70 unkown Y N mostly bad not really
5830 Kara Zor-el 1961 67 Krypton Y Y G Yes
43. ID First Name Last Name Birth Year Height Birthplace Identity is secret Can fly Alignment Wears cape
7435 Bruce Wayne 1969 74 Gotham Y N Good black
0958 Ororo Munroe 1979 71 Manhattan N Y Good long
9471 Diana Trevor 1618 68 Paradise Island Y N Good rarely
9483 Janet Van Dyne 1942 64 Cresskill N N Good not really
0696 Peter Parker 1983 70 Queens Y N Good never
5531 Harleen Quinzell 1981 62 Gotham Y N Bad no
4734 Erik Lehnsherr 1932 72 Hamburg N N Bad Absolutely
7757 Natasha Romanova 1983 67 St. Petersburg N N Good No way
0323 Jean Grey 1977 66 Annandale N N Good Mostly not
3990 Clark Kent 1954 76 Krypton Y Y Good always
3057 Victor Von Doom 1943 74 Latveria N N Bad yes
0573 Stephen Strange 1968 74 Philadelphia N N Good Y
7452 Thor Odinson -2287 78 Norway N Y Good Of course
1437 Selina Kyle 1998 67 Gotham Y N Neutral It clashes
1883 Raven Darkolme 1911 70 unkown Y N Bad not really
5830 Kara Zor-el 1961 67 Krypton Y Y Good Yes
44. ID First Name Last Name Birth Year Height Birthplace Identity is secret Can fly Alignment Wears cape
7435 Bruce Wayne 1969 74 Gotham Y N Good Y
0958 Ororo Munroe 1979 71 Manhattan N Y Good Y
9471 Diana Trevor 1618 68 Paradise Island Y N Good N
9483 Janet Van Dyne 1942 64 Cresskill N N Good N
0696 Peter Parker 1983 70 Queens Y N Good N
5531 Harleen Quinzell 1981 62 Gotham Y N Bad N
4734 Erik Lehnsherr 1932 72 Hamburg N N Bad Y
7757 Natasha Romanova 1983 67 St. Petersburg N N Good N
0323 Jean Grey 1977 66 Annandale N N Good N
3990 Clark Kent 1954 76 Krypton Y Y Good Y
3057 Victor Von Doom 1943 74 Latveria N N Bad Y
0573 Stephen Strange 1968 74 Philadelphia N N Good Y
7452 Thor Odinson -2287 78 Norway N Y Good Y
1437 Selina Kyle 1998 67 Gotham Y N Neutral N
1883 Raven Darkolme 1911 70 unkown Y N Bad N
5830 Kara Zor-el 1961 67 Krypton Y Y Good Y