HH Data Science Meetup: Explaining complex machine learning models with LIME
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This document is a graph showing interpretability on the y-axis and complexity on the x-axis. It shows that as models increase in complexity, their interpretability decreases, with more complex models being less interpretable. Simple models are highly interpretable but less complex while more complex models are less interpretable.
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Generative Adversarial Network(GAN)은 2014년 Ian Goodfellow에 의해 처음으로 제안되었으며, 적대적 학습을 통해 실제 데이터의 분포를 추정하는 생성 모델입니다. 최근 들어 GAN은 가장 인기있는 연구 분야로 떠오르고 있고 하루에도 수 많은 관련 논문들이 쏟아져 나오고 있습니다.
수 없이 쏟아져 나오고 있는 GAN 논문들을 다 읽기가 힘드신가요? 괜찮습니다. 기본적인 GAN만 완벽하게 이해한다면 새로 나오는 논문들도 쉽게 이해할 수 있습니다.
이번 발표를 통해 제가 GAN에 대해 알고 있는 모든 것들을 전달해드리고자 합니다. GAN을 아예 모르시는 분들, GAN에 대한 이론적인 내용이 궁금하셨던 분들, GAN을 어떻게 활용할 수 있을지 궁금하셨던 분들이 발표를 들으면 좋을 것 같습니다.
발표영상: https://youtu.be/odpjk7_tGY0
알아두면 쓸데있는 신기한 강화학습 NAVER 2017
발표 영상: http://tv.naver.com/v/2051482
PDF를 받아서 보셔야 깨끗하게 보입니다.
최근 강화학습 트렌드를 다섯 가지로 나눠 설명하고, 데브시스터즈에서 풀고 있는 세 가지 강화학습 문제들을 공유합니다.
PFP：材料探索のための汎用Neural Network Potential_中郷_20220422POLセミナー
2022年4月22日POL共催セミナーでの講演資料です。
https://peatix.com/event/3202191
本資料の前半では、NNP研究の流れを振り返り、どのようにNNP開発のためのデータセット収集やNeural Networkの取扱いが発展してきたのかを紹介
しています。そして後半では、多種多様な系に対して適用可能となるように開発した汎用NNPであるPFPについての解説を行っております。また、PFPを用いた事例としてどのような材料探索が行えるかをその応用的な使い方とともに紹介しております。
Preferred Computational Chemistryウェブサイト
https://matlantis.com/ja/
Preferred Computational Chemistryツイッターアカウント
https://twitter.com/matlantis_pfcc
MLOps in action
Machine Learning operations brings data science to the world of devops. Data scientists create models on their workstations. MLOps adds automation, validation and monitoring to any environment including machine learning on kubernetes. In this session you hear about latest developments and see it in action.
Amazonでのレコメンド生成における深層学習とAWS利用について
2016/10/22 第5回 Machine Learning 15minutes! での講演「Amazonでのレコメンド生成における深層学習とAWS利用について」の資料です。
Graph-Based Network Topology Analysis for Telecom Operators
Graph-Based Network Topology Analysis for Telecom Operators
汎用ニューラルネットワークポテンシャル「PFP」による材料探索_MRS-J2021招待講演_2021/12/15
日本MRS年次大会での講演資料です。
https://www.mrs-j.org/meeting2021/jp/
汎用ニューラルネットワークポテンシャル「PFP」に関して、技術の基本的なところから、実際の活用事例まで紹介しております。
Preferred Computational Chemistryウェブサイト
https://matlantis.com/ja/
End to end Machine Learning using Kubeflow - Build, Train, Deploy and Manage
With the breadth of sheer functionalities which need to be addressed in the Machine Learning world around building, training, serving and managing models, getting it done in a consistent, composable, portable, and scalable manner is hard. The Kubernetes framework is well suited to address these issues, which is why it's a great foundation for deploying ML workloads. Kubeflow is designed to take advantage of these benefits. In this talk, we are going to address how to make it easy for everyone to develop, deploy, and manage portable, scalable ML everywhere and support the full lifecycle Machine Learning using open source technologies like Kubeflow, Tensorflow, PyTorch,Tekton, Knative, Istio and others. We are going to discuss how to enable distributed training of models, model serving, canary rollouts, drift detection, model explainability, metadata management, pipelines and others. Additionally we will discuss Watson productization in progress based on Kubeflow Pipelines and Tekton, and point to Kubeflow Dojo materials and follow-on workshops.
ML-Ops: Philosophy, Best-Practices and Tools
What MLOps is and why it is crucial for the efficiency of your ML developments.
Custom DevOps Monitoring System in MelOn (with InfluxDB + Telegraf + Grafana)
2016년도 데이터야놀자에서 발표한 자료입니다.
멜론에서 InfluxDB + Telegraf + Grafana 조합으로 모니터링 시스템을 구축하고 활용한 사례를 발표한 내용입니다. 다양한 메트릭데이터와 DevOps 측면의 활용 가치에 대해서도 생각해 볼 수 있을 것 같습니다.
（修正）機械学習デザインパターン（ML Design Patterns）の解説
鷲崎弘宜, "機械学習デザインパターン（ML Design Patterns）の解説", スマートエスイー ＆ JST未来社会 eAIセミナー: 機械学習デザインパターン, 2021年3月30日
MLOps with serverless architectures (October 2018)
Talk @ AWS Loft Stockholm, 23/10/2018
But why?
A quick recap on Amazon SageMaker
A quick recap on serverless architectures
Open Source tools: AWS Chalice, Serverless Framework
Demos
Resources
What's hot (20)
グラフ構造データに対する深層学習〜創薬・材料科学への応用とその問題点〜 (第26回ステアラボ人工知能セミナー)
グラフ構造データに対する深層学習〜創薬・材料科学への応用とその問題点〜 (第26回ステアラボ人工知能セミナー)
PFP：材料探索のための汎用Neural Network Potential - 2021/10/4 QCMSR + DLAP共催
PFP：材料探索のための汎用Neural Network Potential - 2021/10/4 QCMSR + DLAP共催
Government GraphSummit: Leveraging Graphs for AI and ML
Government GraphSummit: Leveraging Graphs for AI and ML
PFP：材料探索のための汎用Neural Network Potential_中郷_20220422POLセミナー
PFP：材料探索のための汎用Neural Network Potential_中郷_20220422POLセミナー
Graph-Based Network Topology Analysis for Telecom Operators
Graph-Based Network Topology Analysis for Telecom Operators
汎用ニューラルネットワークポテンシャル「PFP」による材料探索_MRS-J2021招待講演_2021/12/15
汎用ニューラルネットワークポテンシャル「PFP」による材料探索_MRS-J2021招待講演_2021/12/15
End to end Machine Learning using Kubeflow - Build, Train, Deploy and Manage
End to end Machine Learning using Kubeflow - Build, Train, Deploy and Manage
Custom DevOps Monitoring System in MelOn (with InfluxDB + Telegraf + Grafana)
Custom DevOps Monitoring System in MelOn (with InfluxDB + Telegraf + Grafana)
MLOps with serverless architectures (October 2018)
MLOps with serverless architectures (October 2018)
More from Shirin Elsinghorst
Transparente und Verantwortungsbewusste KI
Transparente und Verantwortungsbewusste KI: Was bedeutet Explainable und Responsible AI und warum sollten wir uns damit beschäftigen?
In dieser Session widmen wir uns den aktuellen Themen Explainable AI (XAI) und Responsible AI (RAI), bei denen es um die Entwicklung von Künstlicher Intelligenz geht, die transparent, erklärbar und ethisch verantwortlich agiert. Wir wollen ein breites Publikum ansprechen, von KI-Experten und Forschern bis hin zu Vertretern aus Wirtschaft, Politik und der Zivilgesellschaft. Ziel ist es, ein Verständnis für die Bedeutung von Explainable und Responsible AI zu schaffen, mögliche Herausforderungen zu diskutieren und Lösungsansätze für eine verantwortungsvolle KI-Nutzung zu entwickeln.
Datenstrategie in der Praxis
Die digitale Transformation und der Aufstieg von Künstlicher Intelligenz (KI) haben die Geschäftswelt revolutioniert. Unternehmen, die ihre Geschäftsentscheidungen auf Daten und KI stützen, gewinnen einen klaren Wettbewerbsvorteil und schaffen innovative Lösungen für ihre Kunden. Diese Veranstaltung richtet sich an Führungskräfte, Manager, Unternehmer und Data-Enthusiasten, die mehr darüber erfahren möchten, ob und wann sie eine Datenstrategie brauchen, wie sie eine effektive Datenstrategie entwickeln und wie sie damit ein datengetriebenes und KI-basiertes Business aufbauen oder bereichern können.
RICHTIG GUT: DIE QUALITÄT VON MODELLEN VERSTEHEN
Vortrag auf der M3 Online-Konferenz am 16.06.2020 (https://online.m3-konferenz.de/lecture.php?id=12337&source=0)
Mit Machine Learning getroffene Entscheidungen sind inhärent schwierig – wenn nicht gar unmöglich – nachzuvollziehen. Ein scheinbar gutes Ergebnis mit Hilfe von maschinellen Lernverfahren ist oft schnell erzielt oder wird von anderen als bahnbrechend verkauft.
Die Komplexität einiger der besten Modelle wie neuronaler Netze ist genau das, was sie so erfolgreich macht. Aber es macht sie gleichzeitig zu einer Black Box. Das kann problematisch sein, denn Geschäftsführer oder Vorstände werden weniger geneigt sein, einer Entscheidung zu vertrauen und nach ihr zu handeln, wenn sie sie nicht verstehen.
Shapley Values, Local Interpretable Model-Agnostic Explanations (LIME) und Anchors sind Ansätze, diese komplexen Modelle zumindest teilweise nachvollziehbar zu machen.
In diesem Vortrag erkläre ich, wie diese Ansätze funktionieren, und zeige Anwendungsbeispiele.
LERNZIELE
* Die Teilnehmer erhalten Einblick in Möglichkeit, die komplexe Modelle erklärbar machen.
* Sie lernen, Datensätze kritisch zu hinterfragen und angemessen aufzuteilen.
* Und sie erfahren, unter welchen Bedingungen sie Entscheidungen durch Machine Learning vertrauen können.
Real-World Data Science (Fraud Detection, Customer Churn & Predictive Mainten...
These are slides from a lecture I gave at the School of Applied Sciences in Münster. In this lecture, I talked about **Real-World Data Science** at showed examples on **Fraud Detection, Customer Churn & Predictive Maintenance**.
SAP webinar: Explaining Keras Image Classification Models with LIME
Keras is a high-level open-source deep learning framework that by default works on top of TensorFlow. Keras is minimalistic, efficient and highly flexible because it works with a modular layer system to define, compile and fit neural networks. It has been written in Python but can also be used from within R. Because the underlying backend can be changed from TensorFlow to Theano and CNTK (with more options being developed right now) it is designed to be framework-independent. Models can be trained on CPU or GPU, locally or in the cloud.
I will show an example how to build an image classifier with Keras. We'll be using a convolutional neural net to classify fruits in images. But that's not all! We not only want to judge our black-box model based on accuracy and loss measures - we want to get a better understanding of how the model works. We will use an algorithm called LIME (local interpretable model-agnostic explanations) to find out what part of the different test images contributed most strongly to the classification that was made by our model. I will introduce LIME and explain how it works. And finally, I will show how to apply LIME to the image classifier we built before, as well as to a pretrained Imagenet model.
You will get:
* an introduction to Keras
* an overview about deep learning and neural nets
* a demo how to build an image classifier with Keras
* an introduction to explaining black box models, specifically to the LIME algorithm
* a demo how to apply LIME to explain the predictions of our own Keras image classifier, as well as of a pretrained Imagenet
Further Information:
* www.shirin-glander.de<http://www.shirin-glander.de>
* https://blog.codecentric.de/author/shirin-glander/
* www.youtube.com/codecentricAI
Workshop - Introduction to Machine Learning with R
These are the slides from workshop: Introduction to Machine Learning with R which I gave at the University of Heidelberg, Germany on June 28th 2018.
The accompanying code to generate all plots in these slides (plus additional code) can be found on my blog: https://shirinsplayground.netlify.com/2018/06/intro_to_ml_workshop_heidelberg/
The workshop covered the basics of machine learning. With an example dataset I went through a standard machine learning workflow in R with the packages caret and h2o:
- reading in data
- exploratory data analysis
- missingness
- feature engineering
- training and test split
- model training with Random Forests, Gradient Boosting, Neural Nets, etc.
- hyperparameter tuning
Deep learning - a primer
This is a slide deck from a presentation, that my colleague Uwe Friedrichsen (https://www.slideshare.net/ufried/) and I did together. As we created our respective parts of the presentation on our own, it is quite easy to figure out who did which part of the presentation as the two slide decks look quite different ... :)
For the sake of simplicity and completeness, Uwe copied the two slide decks together. As he did the "surrounding" part, he added my part at the place where I took over and then added concluding slides at the end. Well, I'm sure, you will figure it out easily ... ;)
The presentation was intended to be an introduction to deep learning (DL) for people who are new to the topic. It starts with some DL success stories as motivation. Then a quick classification and a bit of history follows before the "how" part starts.
The first part of the "how" is some theory of DL, to demystify the topic and explain and connect some of the most important terms on the one hand, but also to give an idea of the broadness of the topic on the other hand.
After that the second part dives deeper into the question how to actually implement DL networks. This part starts with coding it all on your own and then moves on to less coding step by step, depending on where you want to start.
The presentation ends with some pitfalls and challenges that you should have in mind if you want to dive deeper into DL - plus the invitation to become part of it.
As always the voice track of the presentation is missing. I hope that the slides are of some use for you, though.
HH Data Science Meetup: Explaining complex machine learning models with LIME
Unfortunately, slideshare doesn't allow re-uploading slides any more, so there is an updated version with some corrected errors here: https://www.slideshare.net/ShirinGlander/hh-data-science-meetup-explaining-complex-machine-learning-models-with-lime-94218890
Ruhr.PY - Introducing Deep Learning with Keras and Python
Ruhr.PY - Python Developer Meetup:
Keras is a high-level API written in Python for building and prototyping neural networks. It can be used on top of TensorFlow, Theano or CNTK. In this talk we build, train and visualize a Model using Python and Keras - all interactive with Jupyter Notebooks!
https://www.meetup.com/Ruhr-py/events/248093628/
-- slide deck generated with beautiful.ai --
-- video recording can be seen here: https://youtu.be/Q8hVXnpEPmc --
-- comment here: https://shirinsplayground.netlify.com/2018/04/ruhrpy_meetup_2018_slides/ --
From Biology to Industry. A Blogger’s Journey to Data Science.
What does blogging mean for Data Sciences?
What is Big Data today?
How to become a Data Scientist and what type of work results from this transformation?
More from Shirin Elsinghorst (10)
Real-World Data Science (Fraud Detection, Customer Churn & Predictive Mainten...
Real-World Data Science (Fraud Detection, Customer Churn & Predictive Mainten...
SAP webinar: Explaining Keras Image Classification Models with LIME
SAP webinar: Explaining Keras Image Classification Models with LIME
Workshop - Introduction to Machine Learning with R
Workshop - Introduction to Machine Learning with R
HH Data Science Meetup: Explaining complex machine learning models with LIME
HH Data Science Meetup: Explaining complex machine learning models with LIME
Ruhr.PY - Introducing Deep Learning with Keras and Python
Ruhr.PY - Introducing Deep Learning with Keras and Python
From Biology to Industry. A Blogger’s Journey to Data Science.
From Biology to Industry. A Blogger’s Journey to Data Science.
Recently uploaded
Leading Change strategies and insights for effective change management pdf 1.pdf
Leading Change strategies and insights for effective change management pdf 1.pdf
State of ICS and IoT Cyber Threat Landscape Report 2024 preview
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/
Quantum Computing: Current Landscape and the Future Role of APIs
The current state of quantum computing and the role of APIs to take it mainstream.
Enhancing Performance with Globus and the Science DMZ
ESnet has led the way in helping national facilities—and many other institutions in the research community—configure Science DMZs and troubleshoot network issues to maximize data transfer performance. In this talk we will present a summary of approaches and tips for getting the most out of your network infrastructure using Globus Connect Server.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...
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.
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
GraphRAG is All You need? LLM & Knowledge Graph
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/
Accelerate your Kubernetes clusters with Varnish Caching
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.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Essentials of Automations: The Art of Triggers and Actions in FME
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
UiPath Test Automation using UiPath Test Suite series, part 4
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
Pushing the limits of ePRTC: 100ns holdover for 100 days
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Securing your Kubernetes cluster_ a step-by-step guide to success !
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...
💥 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
The Metaverse and AI: how can decision-makers harness the Metaverse for their...
The Metaverse is popularized in science fiction, and now it is becoming closer to being a part of our daily lives through the use of social media and shopping companies. How can businesses survive in a world where Artificial Intelligence is becoming the present as well as the future of technology, and how does the Metaverse fit into business strategy when futurist ideas are developing into reality at accelerated rates? How do we do this when our data isn't up to scratch? How can we move towards success with our data so we are set up for the Metaverse when it arrives?
How can you help your company evolve, adapt, and succeed using Artificial Intelligence and the Metaverse to stay ahead of the competition? What are the potential issues, complications, and benefits that these technologies could bring to us and our organizations? In this session, Jen Stirrup will explain how to start thinking about these technologies as an organisation.
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Recently uploaded (20)
Leading Change strategies and insights for effective change management pdf 1.pdf
Leading Change strategies and insights for effective change management pdf 1.pdf
State of ICS and IoT Cyber Threat Landscape Report 2024 preview
State of ICS and IoT Cyber Threat Landscape Report 2024 preview
Quantum Computing: Current Landscape and the Future Role of APIs
Quantum Computing: Current Landscape and the Future Role of APIs
FIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdf
FIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdf
Enhancing Performance with Globus and the Science DMZ
Enhancing Performance with Globus and the Science DMZ
Transcript: Selling digital books in 2024: Insights from industry leaders - T...
Transcript: Selling digital books in 2024: Insights from industry leaders - T...
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™
Accelerate your Kubernetes clusters with Varnish Caching
Accelerate your Kubernetes clusters with Varnish Caching
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Free Complete Python - A step towards Data Science
Free Complete Python - A step towards Data Science
Essentials of Automations: The Art of Triggers and Actions in FME
Essentials of Automations: The Art of Triggers and Actions in FME
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Monitoring Java Application Security with JDK Tools and JFR Events
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UiPath Test Automation using UiPath Test Suite series, part 4
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Pushing the limits of ePRTC: 100ns holdover for 100 days
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The Metaverse and AI: how can decision-makers harness the Metaverse for their...
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Generative AI Deep Dive: Advancing from Proof of Concept to Production
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