DSDT Meetup June 2018

•

0 likes•33 views

AI, Machine Learning, Deep Learning: Stop talking and start acting! Agenda : 6:00 - 6:15 PM: Welcome & Networking 6:15 - 6:30 PM: News and Introduction 6:30 – 7:15 PM: How to Get Started With AI 7:15 - 8:00 PM: Commit Assistant – AI to predict and prevent bugs in Ubisoft’s code 8:00 - 8:30 PM: Networking

Technology

Agenda
3
6:15 - 6:30 Meetup News
6:30 - 7:15 (JDA Labs)
How to Get Started With AI
7:15 - 8:00 (Ubisoft)
AI to predict and prevent bugs in code
8:00 - 8:30: Q&A + Networking

Meetup News
4
• June 7: IT Connect Conference - IoT (Seaway Park - St Lambert)
• June 8: Hands-on IoT Workshop (Catallaxy)
• June 12: DataViz User Group #3 (Microsoft)
• June 14: RDV Techno: IoT + AI + Health (Desjardins Labs)
• June 14-15-16: Frontière IA (Le Virage MTL)
• June 18: HackerNest Tech Socials (Spaces Mile End)
• June 19: Google Cloud Platform Fundamentals (Free, women only, Centre Phi)
• June 19: Data Driven Montreal: Data Warehousing vs Data Visualization (OVH)
• June 21: Google Cloud Platform Meetup (Shopify)

5
DSDT Meetup
is 1-year old.
Happy Birthday !!
(More to come next month…)

Topic #1
How to Get Started
With AI
Data Science | Design | Technology 6

Tools to Get Started with Machine
Learning / AI
Christian Hudon | JDA Labs
Twitter: @christian_hudon and @JDALabsMTL
1 / 27

You think machine learning is cool

2 / 27

But what about after that?
Maybe you have a rst project idea...
5 / 27

But what about after that?
Maybe you have a rst project idea...
(Or maybe not.)
6 / 27

Classical machine learning:
Scikit-learn
For NLP: SpaCy
14 / 27

Deep learning:
Pytorch (and Skorch)
* Keras (with Tensor ow) also a reasonable choice.
15 / 27

Visualization (simpler / pre-canned):
Pandas & Seaborn
16 / 27

Visualization (expressive):
HoloViews (or Altair)
17 / 27

How to continue learning?
Do a project!
18 / 27

Dataset Sources
The Open Data movement (Bixi, etc.)
19 / 27

Dataset Sources
The Open Data movement (Bixi, etc.)
Kaggle
20 / 27

Dataset Sources
The Open Data movement (Bixi, etc.)
Kaggle
Your own life! (FitBit, your bank)
21 / 27

Dataset Sources
The Open Data movement (Bixi, etc.)
Kaggle
Your own life! (FitBit, your bank)
The Internet!
22 / 27

Project Ideas
Forecast Bixi usage
23 / 27

Project Ideas
Forecast Bixi usage
Cat vs. dog detector
24 / 27

Project Ideas
Forecast Bixi usage
Cat vs. dog detector
Automated categorization of credit card bill items
25 / 27

After your rst project
Getting better at Python: https://www.youtube.com/playlist?list=PLu5kChXP9Hipqr1gIROxGlvb3YVadOjUB
(14 videos currently)
Learning the basics of classes and object-oriented programming:
http://www.greenteapress.com/thinkpython/html/thinkpython016.html (and next two chapters)
The matrix algebra you need for machine learning: http://parrt.cs.usfca.edu/doc/matrix-calculus/
Scaling with Dask and Dask-ml and Dask-Kubernetes
Learning the basics of SQL
26 / 27

Thank you!
Slides and notebooks:
http://christianhudon.name/talks/#DSDT-2018-06
Tools
Trying things out: Jupyter Lab
Numerical computations: Numpy
Data Exploration: Pandas
Classical machine learning: Scikit-learn (and
SpaCy)
Deep learning: Pytorch (and Skorch)
Visualization: Pandas and Seaborn
More Visualization: HoloViews (or Altair)
27 / 27

Topic # 2
AI to predict and
prevent bugs in
code
Data Science | Design | Technology 1
Ubisoft
La Forge

Merci / Thank You
1
@jdalabsmtl
Data Science | Design | Technology
(Check for next DSDT meetup at https://www.meetup.com/DSDTMTL)

Similar to DSDT Meetup June 2018

Big Data has been popular last 10 years using Hadoop and Spark for data analysis and prediction with large scale data sets in distributed parallel computing systems. Its platform has expanded using NoSQL DB and Search Engine as well and has been more popular along cloud computing. Then, Deep Learning has become a buzzword past several years using GPU and Big Data. It makes even small companies and labs to own supercomputers with a small amount of budgets, which is the situation of “Dream Comes True” in the IT and business. In this talk, the history and trends of Big Data and AI platforms are introduced and how predictive analysis should be presented in Business using Big Data & AI.

Introduction to Big Data and AI for Business Analytics and Prediction

Jongwook Woo

This talk aims at providing insights, performance, and architecture on Financial Fraud Detection on a mobile money transactional activity in Azure ML and Spark. We have predicted and classified the transaction as normal or fraud with a small sample and massive data set using Azure ML and Spark ML, which are traditional systems and Big Data respectively. I will present predictive analysis with several classification models experimenting in Azure and Spark ML. Besides, scalability of Spark ML will be presented for the models with different number of nodes for Spark clusters in Amazon AWS.

Predictive Analysis of Financial Fraud Detection using Azure and Spark ML

Jongwook Woo

Shaping our AI (Strategy)?

Thammasat University, Musashino University

History and Trend of Big Data and Deep Learning

Jongwook Woo

Introduction to Data Science (Data Science Thailand Meetup #1)

Data Science Thailand

Smart data are essential when faced with massive-scale data collections. "Smart" refers to data that are tagged or indexed with meaning-filled metadata that carry information about the semantic meaning of the data, its applications, use cases, content, context, and more. Such meta-tags enable efficient and effective discovery, description, and delivery of the right data at the right time, both to humans and to automatic processes. Kirk Borne is a data scientist and an astrophysicist who has used his talents at Booz Allen since 2015. He was professor of astrophysics and computational science at George Mason University (GMU) for 12 years. He served as undergraduate advisor for the GMU data science program and graduate advisor in the computational science and informatics Ph.D. program.

DN2017 | From Big Data to Smart Data | Kirk Borne | Booz Allen Hamilton

Dataconomy Media

Introduction to Big Data and its Trends

Jongwook Woo

Big data. Small data. All data. You have access to an ever-expanding volume of data inside the walls of your business and out across the web. The potential in data is endless – from predicting election results to preventing the spread of epidemics. But how can you use it to your advantage to help move your business forward? Data is growing exponentially and it’s now possible to mine and unlock insights from data in new and unexpected ways. Empower your business to take advantage of this data by harnessing the rich capabilities of Microsoft SQL Server and the familiarity of Microsoft Office to help organize, analyze, and make sense of your data—no matter the size.

Data Culture Series - Keynote & Panel - Birmingham - 8th April 2015

Jonathan Woodward

Charles Cai has more than two decades of experience and track records of global transformational programme deliveries – from vision, evangelism to end-to-end execution in global investment banks, and energy trading companies, where he excels at designing and building innovative, large scale, Big Data systems in high volume low latency trading, global Energy Trading & Risk Management, and advanced temporal and geospatial predictive analytics, as Chief Front Office Technical Architect and Head of Data Science. He’s also a frequent speaker at Google Campus, Big Data Innovation Summit, Cloud World Forum, Data Science London, QCon London and MoD CIO Symposium etc, to promote knowledge and best practice sharing, with audience ranging from developers, data scientists, to CXO level senior executives from both IT and business background. He has in-depth knowledge and experience Scala, Python, C# / F#, C++, Node.js, Java, R, Haskell programming languages in Mobile, Desktop, Hadoop/Spark, Cloud IoT/MCU and BlockChain etc, and TOGAF9, EMC-DS, AWS CNE4 etc. certifications.

MAKING SENSE OF IOT DATA W/ BIG DATA + DATA SCIENCE - CHARLES CAI

Big Data Week

How to become the best datascientist in Europe

DigitYser

Kaggle Competitions, New Friends, New Skills and New Opportunities

Jo-fai Chow

From Data to Visualization, what happens in between?

Krist Wongsuphasawat

Data are pouring in, and defining and providing data-processing services at massive scale, in short, Big Data services, could significantly improve the revenue of Europe's Small and Medium Enterprises (SMEs). A paradigm shift is about occur, one in which data processing becomes a basic life utility, for both SMEs and the European people. Although the burgeoning datacenter industry, of which the Netherlands is a top player in Europe, is promising to enable Big Data services, the architectures and even infrastructure for these services are still lagging behind in performance, efficiency, and sophistication, and are built as monoliths reminding us of traditional data silos. Can we remove the performance and efficiency limitations of the current Big Data ecosystems, that is, of the complex stacks of middleware that are currently in use, for Big Data services? In this talk, I will present several use cases (workloads) of Big Data services for time-stamped [2,3] and graph data [4], evaluate or benchmark the performance of several Big Data stacks [3,4] for these use-cases, and present a path (and promising early results) to providing a generic, data-agnostic, non-monolithic Big Data architecture that can efficiently and elastically use datacenter resources via cloud computing interfaces [1,5]. [1] A. L. Varbanescu and A. Iosup, On Many-Task Big Data Processing: from GPUs to Clouds. Proc. of SC|12 (MTAGS).? http://www.pds.ewi.tudelft.nl/~iosup/many-tasks-big-data-vision13mtags_v100.pdf [2] de Ruiter and Iosup. A workload model for MapReduce. MSc thesis at TU Delft. Jun 2012. Available online via TU Delft Library, http://library.tudelft.nl [3] Hegeman, Ghit, Capotã, Hidders, Epema, Iosup. The BTWorld Use Case for Big Data Analytics: Description, MapReduce Logical Workflow, and Empirical Evaluation. IEEE Big Data 2013. http://www.pds.ewi.tudelft.nl/~iosup/btworld-mapreduce-workflow13ieeebigdata.pdf [4] Y. Guo, M. Biczak, A. L. Varbanescu, A. Iosup, C. Martella, and T. L. Willke. How Well do Graph-Processing Platforms Perform? An Empirical Performance Evaluation and Analysis. IEEE IPDPS 2014. http://www.pds.ewi.tudelft.nl/~iosup/perf-eval-graph-proc14ipdps.pdf [5] B. Ghit, N. Yigitbasi, A. Iosup, and D. Epema. Balanced Resource Allocations Across Multiple Dynamic MapReduce Clusters. ACM SIGMETRICS 2014. http://pds.twi.tudelft.nl/~iosup/dynamic-mapreduce14sigmetrics.pdf

Big Data in the Cloud: Enabling the Fourth Paradigm by Matching SMEs with Dat...

Alexandru Iosup

Introduction to Processing

siufu

Abstract: Experimental and computational research is gaining more and more interest in the last decades in the field of social science and economics. Conducting laboratory experiments and incorporating heterogeneity within agent-based models help us get a better understanding of the analyzed phenomena and the micro-macro rules driving them by taking the human factor into account -- either directly or through stylized personal preferences. Our contribution is a new tool called the Participatory Extension Module v2.0 which is intended to help scientists conducting mixed-method research (i.e., perform experimental research using existing agent-based models). It is an improved version of the original PET [1], a robust and generic web framework that allows modellers to extend their models to participatory simulations. It is a set of web applications that incorporates agent-based simulations into a web interface compatible with any of the major web browsers, enabling users to administrate, run and participate in simulations in a way that they are familiar with, applying the mechanisms and practices they use every day while browsing web-pages and using other web-based applications. Applications of PET v2.0 may include online case studies for demonstrative and teaching purposes, or the conduct of lab experiments for behavioural studies of a model. The presentation includes a hands-on live demo of the features of the framework using a widely known model. [1] Ivanyi, Marton, Rajmund Bocsi, Laszlo Gulyas, Vilmos Kozma and Richard Legendi. "The multi-agent simulation suite." In Emergent Agents and Socialities: Social and Organizational Aspects of Intelligence. Papers from the 2007 AAAI Fall Symposium, pp. 57-64. 2007.

When Experimental and Computational Research Meet: The Participatory Extensio...

Richard Oliver Legendi

DSDT Meetup November 2018

DSDT_MTL

The Ai & I at Work

Tarek Hoteit

A New Paradigm on Analytic-Driven Information and Automation V2.pdf

ArmyTrilidiaDevegaSK

In this talk, I will introduce the future technical and social impact of open data on the open platform. Nowadays, web technology, whose process of standardization is open and independent of vendors, has evolved into an application platform on various devices such as PC, mobile and embedded devices. Developers of web-standards and web-based application are now trying to integrate a map technology tightly into the web standards for the future mobile web-based applications. As an organizer of a workshop aiming to discuss the future web-based map technology, I will introduce emerging technology and discussion in this area. At the workshop I held in Tokyo, Japan in 2012, many developers and researchers in various communities such as OpenStreetMap, FOSS4G, HTML5, and Linked OpenData gathered and had an enthusiastic discussion. I will summarize the discussion and future perspective at the workshop in my talk in order to discuss future technical and social possibility of OpenStreetMap on web-based open platform.

Open Data on the Open Platform

Masaki Ito

SuanIct-Bigdata desktop-final

stelligence

Similar to DSDT Meetup June 2018 (20)

Introduction to Big Data and AI for Business Analytics and Prediction

Predictive Analysis of Financial Fraud Detection using Azure and Spark ML

Shaping our AI (Strategy)?

History and Trend of Big Data and Deep Learning

Introduction to Data Science (Data Science Thailand Meetup #1)

DN2017 | From Big Data to Smart Data | Kirk Borne | Booz Allen Hamilton

Introduction to Big Data and its Trends

Data Culture Series - Keynote & Panel - Birmingham - 8th April 2015

MAKING SENSE OF IOT DATA W/ BIG DATA + DATA SCIENCE - CHARLES CAI

How to become the best datascientist in Europe

Kaggle Competitions, New Friends, New Skills and New Opportunities

From Data to Visualization, what happens in between?

Big Data in the Cloud: Enabling the Fourth Paradigm by Matching SMEs with Dat...

Introduction to Processing

When Experimental and Computational Research Meet: The Participatory Extensio...

DSDT Meetup November 2018

The Ai & I at Work

A New Paradigm on Analytic-Driven Information and Automation V2.pdf

Open Data on the Open Platform

SuanIct-Bigdata desktop-final

More from DSDT_MTL

Time series in financial domain: A deep learning approach Agenda: ----------- 3:45pm - 4:00pm: Arrival & Networking 4:00pm - 4:15pm: News & Intro 4:15pm - 5:15pm: Time series in financial domain: A deep learning approach 5:15pm - 5:30pm: Virtual Snack & Networking About the main speaker: --------------------------------- Christophe Pere, Chief Data Scientist (La Capitale Insurance) "I have a Ph.D. in Astrophysics specializing in atmospheric characterization during which I was able to learn the basics of data mining. I then continued my journey through data science and AI in order to understand the principles of learning. I worked on the evaluation of automotive markets and the prediction of vehicle prices with time series and machine learning models. I then joined an institute specialized in autonomous vehicles, the objective was to be able to recreate the real world from sensor data on vehicles traveling in Europe. I finally moved to Canada in the insurance industry to develop an R&D team and work on research projects focused on NLP, time series, and graphs."

DSDT Meetup Septembre 2021

DSDT_MTL

We see twice - or why the fly’s brain is better than our big brains when swapping is involved. Vision came early in evolution; trilobites could see more than 500 million years ago. Yet, our understanding of visual processes is cortex-centric, a recent structure. Similarly, deep-neural network architectures are often loosely based on the visual cortex. One might ask: does a computerk really need to “consciously” see? In this presentation, I will share completely novel findings from brain implant data in rodents showing that there exists an evolutionary ancient brain region, the superior colliculus, that we share with mosquitos, fishes and birds, which can compute complex scenes and can even process categorical information. We will then discuss how we could use this fast acting, wide-reaching circuitry to come up with novel neural network architectures. These new networks could be useful for fast-acting machine-vision models with far reaching potential, such as for scene navigation.

DSDT Meetup August 2021

DSDT_MTL

Tools using AI will affect and, in many cases, redefine most areas of societal impact such as medical practice and intervention, autonomous transportation and law enforcement. While so far, most of the focus and time is invested into optimizing models’ performance, whenever a single wrong prediction has big implications in terms of value or life, accuracy becomes less important than explainability. In this talk, we will learn about explainable AI and we will see how to apply some of the available tools to answer the question ‘’what did my system consider in order to output a specific prediction’.

DSDT meetup July 2021

DSDT_MTL

How can reinforcement learning help us fly balloons in the stratosphere? This talk describes the use of reinforcement learning to create a high-performing flight controller for Loon superpressure balloons. The Google algorithm uses data augmentation and a self-correcting design to overcome the key technical challenge of reinforcement learning from imperfect data, which has proved to be a major obstacle to its application to physical systems. Marc G. Bellemare, from the Google Brain team in Montreal, will present recent work, published in Nature, on using reinforcement learning to fly tennis-court-sized balloons in the stratosphere. Agenda: ----------- 3:45pm - 4:00pm: Arrival & Networking 4:00pm - 4:15pm: News & Intro 4:15pm - 5:15pm: RL to fly balloons in the stratosphere 5:15pm - 5:30pm: Virtual Snack & Networking

DSDT Meetup May 2021

DSDT_MTL

Building a robust machine learning model is not an easy task. After all, most POCs don't make it into production. And even if they make it into production, you still need to monitor its performance. How can you build performant, tolerant, stable, predictive models that have known and fair biases? How can you make sure your models yield their value over time and stay performant after your team has deployed them? What are the current practices of model validation (or lack of), how are they flawed, and how could we improve them? Simon Dagenais from Snitch AI will go through the reasons behind using an efficient validation framework that goes beyond the common metrics used by ML practitioners and why these tests matter when building high-quality models. Agenda: ----------- 3:45pm - 4:00pm: Arrival & Networking 4:00pm - 4:15pm: News & Intro 4:15pm - 5:15pm: How to QA your ML models 5:15pm - 5:30pm: Virtual Snack & Networking About the main speaker: --------------------------------- Simon Dagenais is the Lead Data Scientist at Snitch AI, a machine learning validation tool. Before working on Snitch AI, Simon was a data scientist consultant at Moov AI, the parent company of Snitch AI. During his time as a consultant, he built and deployed custom ML solutions to solve business needs at companies like DRW, Société de Transport de Montréal and Cogeco. He now aspires to solve problems that data science teams will encounter during the course of a ML project cycle. Simon obtained an M.Sc. in economics from HEC Montreal. He frequently speaks in conferences, panels and meetups.

DSDT Meetup April 2021

DSDT_MTL

Artificial intelligence (AI) is dramatically transforming the world of finance Agenda: 6:15 - 6:45: Arrival, Snack & Networking 6:45 - 7:15 PM: News and DSDT Survey Results 7:15 – 8:00 PM: How AI is transforming the world of finance (Nick Vandewiele) 8:00 - 8:30 PM: Wrap-up, Snack & Networking About the main speaker: Nick Vandewiele, PhD is a data scientist in one of the big financial institutions in Montreal. His experience in AI in finance includes financial markets, investments, and credit risk modeling

DSDT Meetup May 2019

DSDT_MTL

DSDT Meetup March 2019

DSDT_MTL

DSDT Meetup February 2019

DSDT_MTL

DSDT Meetup May 2017

DSDT_MTL

DSDT Meetup July 2017

DSDT_MTL

DSDT Meetup October 2017

DSDT_MTL

DSDT Meetup Nov 2017

DSDT_MTL

DSDT Meetup January 2018

DSDT_MTL

DSDT Meetup February 2018

DSDT_MTL

DSDT Meetup May 2018

DSDT_MTL

DSDT Meetup July 2018

DSDT_MTL

More from DSDT_MTL (16)

DSDT Meetup Septembre 2021

DSDT Meetup August 2021

DSDT meetup July 2021

DSDT Meetup May 2021

DSDT Meetup April 2021

DSDT Meetup May 2019

DSDT Meetup March 2019

DSDT Meetup February 2019

DSDT Meetup May 2017

DSDT Meetup July 2017

DSDT Meetup October 2017

DSDT Meetup Nov 2017

DSDT Meetup January 2018

DSDT Meetup February 2018

DSDT Meetup May 2018

DSDT Meetup July 2018

Recently uploaded

[2024]Digital Global Overview Report 2024 Meltwater.pdf

hans926745

The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.

Artificial Intelligence: Facts and Myths

Joaquim Jorge

The 7 Things I Know About Cyber Security After 25 Years | April 2024

Rafal Los

Tech Trends Report 2024 Future Today Institute.pdf

hans926745

As privacy and data protection regulations evolve rapidly, organizations operating in multiple jurisdictions face mounting challenges to ensure compliance and safeguard customer data. With state-specific privacy laws coming up in multiple states this year, it is essential to understand what their unique data protection regulations will require clearly. How will data privacy evolve in the US in 2024? How to stay compliant? Our panellists will guide you through the intricacies of these states' specific data privacy laws, clarifying complex legal frameworks and compliance requirements. This webinar will review: - The essential aspects of each state's privacy landscape and the latest updates - Common compliance challenges faced by organizations operating in multiple states and best practices to achieve regulatory adherence - Valuable insights into potential changes to existing regulations and prepare your organization for the evolving landscape

TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments

TrustArc

GenAI Risks & Security Meetup 01052024.pdf

lior mazor

Three things you will take away from the session: • How to run an effective tenant-to-tenant migration • Best practices for before, during, and after migration • Tips for using migration as a springboard to prepare for Copilot in Microsoft 365 Main ideas: Migration Overview: The presentation covers the current reality of cross-tenant migrations, the triggers, phases, best practices, and benefits of a successful tenant migration Considerations: When considering a migration, it is important to consider the migration scope, performance, customization, flexibility, user-friendly interface, automation, monitoring, support, training, scalability, data integrity, data security, cost, and licensing structure Next Wave: The next wave of change includes the launch of Copilot, which requires businesses to be prepared for upcoming changes related to Copilot and the cloud, and to consolidate data and tighten governance ShareGate: ShareGate can help with pre-migration analysis, configurable migration tool, and automated, end-user driven collaborative governance

Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff

sammart93

Join our latest Connector Corner webinar to discover how UiPath Integration Service revolutionizes API-centric automation in a 'Quote to Cash' process—and how that automation empowers businesses to accelerate revenue generation. A comprehensive demo will explore connecting systems, GenAI, and people, through powerful pre-built connectors designed to speed process cycle times. Speakers: James Dickson, Senior Software Engineer Charlie Greenberg, Host, Product Marketing Manager

Connector Corner: Accelerate revenue generation using UiPath API-centric busi...

DianaGray10

Imagine a world where information flows as swiftly as thought itself, making decision-making as fluid as the data driving it. Every moment is critical, and the right tools can significantly boost your organization’s performance. The power of real-time data automation through FME can turn this vision into reality. Aimed at professionals eager to leverage real-time data for enhanced decision-making and efficiency, this webinar will cover the essentials of real-time data and its significance. We’ll explore: FME’s role in real-time event processing, from data intake and analysis to transformation and reporting An overview of leveraging streams vs. automations FME’s impact across various industries highlighted by real-life case studies Live demonstrations on setting up FME workflows for real-time data Practical advice on getting started, best practices, and tips for effective implementation Join us to enhance your skills in real-time data automation with FME, and take your operational capabilities to the next level.

From Event to Action: Accelerate Your Decision Making with Real-Time Automation

Safe Software

How to Troubleshoot Apps for the Modern Connected Worker

ThousandEyes

This presentation explores the impact of HTML injection attacks on web applications, detailing how attackers exploit vulnerabilities to inject malicious code into web pages. Learn about the potential consequences of such attacks and discover effective mitigation strategies to protect your web applications from HTML injection vulnerabilities. for more information visit https://bostoninstituteofanalytics.org/category/cyber-security-ethical-hacking/

HTML Injection Attacks: Impact and Mitigation Strategies

Boston Institute of Analytics

AWS Community Day CPH - Three problems of Terraform

Andrey Devyatkin

The value of a flexible API Management solution for Open Banking Steve Melan, Manager for IT Innovation and Architecture - State's and Saving's Bank of Luxembourg Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - The value of a flexible API Management solution for O...

apidays

Discord is a free app offering voice, video, and text chat functionalities, primarily catering to the gaming community. It serves as a hub for users to create and join servers tailored to their interests. Discord’s ecosystem comprises servers, each functioning as a distinct online community with its own channels dedicated to specific topics or activities. Users can engage in text-based discussions, voice calls, or video chats within these channels. Understanding Discord Servers Discord servers are virtual spaces where users congregate to interact, share content, and build communities. Servers may revolve around gaming, hobbies, interests, or fandoms, providing a platform for like-minded individuals to connect. Communication Features Discord offers a range of communication tools, including text channels for messaging, voice channels for real-time audio conversations, and video channels for face-to-face interactions. These features facilitate seamless communication and collaboration. What Does NSFW Mean? The acronym NSFW stands for “Not Safe For Work,” indicating content that may be inappropriate for professional or public settings. NSFW Content NSFW content encompasses material that is sexually explicit, violent, or otherwise graphic in nature. It often includes nudity, profanity, or depictions of sensitive topics.

Understanding Discord NSFW Servers A Guide for Responsible Users.pdf

UK Journal

Advantages of Hiring UIUX Design Service Providers for Your Business

Pixlogix Infotech

A Domino Admins Adventures (Engage 2024)

Gabriella Davis

In this session, we will delve into strategic approaches for optimizing knowledge management within Microsoft 365, amidst the evolving landscape of Copilot. From leveraging automatic metadata classification and permission governance with SharePoint Premium, to unlocking Viva Engage for the cultivation of knowledge and communities, you will gain actionable insights to bolster your organization's knowledge-sharing initiatives. In this session, we will also explore how to facilitate solutions to enable your employees to find answers and expertise within Microsoft 365. You will leave equipped with practical techniques and a deeper understanding of how there is more to effective knowledge management than just enabling Copilot, but building actual solutions to prepare the knowledge that Copilot and your employees can use.

Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...

Drew Madelung

Tata AIG General Insurance Company - Insurer Innovation Award 2024

The Digital Insurer

Real Time Object Detection Using Open CV

Khem

💉💊+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHABI}}+971581248768 +971581248768 Mtp-Kit (500MG) Prices » Dubai [(+971581248768**)] Abortion Pills For Sale In Dubai, UAE, Mifepristone and Misoprostol Tablets Available In Dubai, UAE CONTACT DR.Maya Whatsapp +971581248768 We Have Abortion Pills / Cytotec Tablets /Mifegest Kit Available in Dubai, Sharjah, Abudhabi, Ajman, Alain, Fujairah, Ras Al Khaimah, Umm Al Quwain, UAE, Buy cytotec in Dubai +971581248768''''Abortion Pills near me DUBAI | ABU DHABI|UAE. Price of Misoprostol, Cytotec” +971581248768' Dr.DEEM ''BUY ABORTION PILLS MIFEGEST KIT, MISOPROTONE, CYTOTEC PILLS IN DUBAI, ABU DHABI,UAE'' Contact me now via What's App…… abortion Pills Cytotec also available Oman Qatar Doha Saudi Arabia Bahrain Above all, Cytotec Abortion Pills are Available In Dubai / UAE, you will be very happy to do abortion in Dubai we are providing cytotec 200mg abortion pill in Dubai, UAE. Medication abortion offers an alternative to Surgical Abortion for women in the early weeks of pregnancy. We only offer abortion pills from 1 week-6 Months. We then advise you to use surgery if its beyond 6 months. Our Abu Dhabi, Ajman, Al Ain, Dubai, Fujairah, Ras Al Khaimah (RAK), Sharjah, Umm Al Quwain (UAQ) United Arab Emirates Abortion Clinic provides the safest and most advanced techniques for providing non-surgical, medical and surgical abortion methods for early through late second trimester, including the Abortion By Pill Procedure (RU 486, Mifeprex, Mifepristone, early options French Abortion Pill), Tamoxifen, Methotrexate and Cytotec (Misoprostol). The Abu Dhabi, United Arab Emirates Abortion Clinic performs Same Day Abortion Procedure using medications that are taken on the first day of the office visit and will cause the abortion to occur generally within 4 to 6 hours (as early as 30 minutes) for patients who are 3 to 12 weeks pregnant. When Mifepristone and Misoprostol are used, 50% of patients complete in 4 to 6 hours; 75% to 80% in 12 hours; and 90% in 24 hours. We use a regimen that allows for completion without the need for surgery 99% of the time. All advanced second trimester and late term pregnancies at our Tampa clinic (17 to 24 weeks or greater) can be completed within 24 hours or less 99% of the time without the need surgery. The procedure is completed with minimal to no complications. Our Women's Health Center located in Abu Dhabi, United Arab Emirates, uses the latest medications for medical abortions (RU-486, Mifeprex, Mifegyne, Mifepristone, early options French abortion pill), Methotrexate and Cytotec (Misoprostol). The safety standards of our Abu Dhabi, United Arab Emirates Abortion Doctors remain unparalleled. They consistently maintain the lowest complication rates throughout the nation. Our Physicians and staff are always available to answer questions and care for women in one of the most difficult times in their lives. The decision to have an abortion at the Abortion Cl

+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...

?#DUbAI#??##{{(☎️+971_581248768%)**%*]'#abortion pills for sale in dubai@

Recently uploaded (20)

[2024]Digital Global Overview Report 2024 Meltwater.pdf

Artificial Intelligence: Facts and Myths

The 7 Things I Know About Cyber Security After 25 Years | April 2024

Tech Trends Report 2024 Future Today Institute.pdf

TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments

GenAI Risks & Security Meetup 01052024.pdf

Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff

Connector Corner: Accelerate revenue generation using UiPath API-centric busi...

From Event to Action: Accelerate Your Decision Making with Real-Time Automation

How to Troubleshoot Apps for the Modern Connected Worker

HTML Injection Attacks: Impact and Mitigation Strategies

AWS Community Day CPH - Three problems of Terraform

Apidays New York 2024 - The value of a flexible API Management solution for O...

Understanding Discord NSFW Servers A Guide for Responsible Users.pdf

Advantages of Hiring UIUX Design Service Providers for Your Business

A Domino Admins Adventures (Engage 2024)

Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...

Tata AIG General Insurance Company - Insurer Innovation Award 2024

Real Time Object Detection Using Open CV

+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...

DSDT Meetup June 2018

1. Data Science | Design | Technology (June 05, 2018) https://www.meetup.com/DSDTMTL 1

2. 2

3. Agenda 3 6:15 - 6:30 Meetup News 6:30 - 7:15 (JDA Labs) How to Get Started With AI 7:15 - 8:00 (Ubisoft) AI to predict and prevent bugs in code 8:00 - 8:30: Q&A + Networking

4. Meetup News 4 • June 7: IT Connect Conference - IoT (Seaway Park - St Lambert) • June 8: Hands-on IoT Workshop (Catallaxy) • June 12: DataViz User Group #3 (Microsoft) • June 14: RDV Techno: IoT + AI + Health (Desjardins Labs) • June 14-15-16: Frontière IA (Le Virage MTL) • June 18: HackerNest Tech Socials (Spaces Mile End) • June 19: Google Cloud Platform Fundamentals (Free, women only, Centre Phi) • June 19: Data Driven Montreal: Data Warehousing vs Data Visualization (OVH) • June 21: Google Cloud Platform Meetup (Shopify)

5. 5 DSDT Meetup is 1-year old. Happy Birthday !! (More to come next month…)

6. Topic #1 How to Get Started With AI Data Science | Design | Technology 6

7. Tools to Get Started with Machine Learning / AI Christian Hudon | JDA Labs Twitter: @christian_hudon and @JDALabsMTL 1 / 27

8. You think machine learning is cool 2 / 27

9. You've taken a rst online class 3 / 27

10. But what about after that? 4 / 27

11. But what about after that? Maybe you have a rst project idea... 5 / 27

12. But what about after that? Maybe you have a rst project idea... (Or maybe not.) 6 / 27

13. Let's Get Started! 7 / 27

14. 8 / 27

15. 9 / 27

16. Who is this presentation for? 10 / 27

17. Trying things out: Jupyter Lab 11 / 27

18. Numerical computations: Numpy 12 / 27

19. Data Exploration: Pandas 13 / 27

20. Classical machine learning: Scikit-learn For NLP: SpaCy 14 / 27

21. Deep learning: Pytorch (and Skorch) * Keras (with Tensor ow) also a reasonable choice. 15 / 27

22. Visualization (simpler / pre-canned): Pandas & Seaborn 16 / 27

23. Visualization (expressive): HoloViews (or Altair) 17 / 27

24. How to continue learning? Do a project! 18 / 27

25. Dataset Sources The Open Data movement (Bixi, etc.) 19 / 27

26. Dataset Sources The Open Data movement (Bixi, etc.) Kaggle 20 / 27

27. Dataset Sources The Open Data movement (Bixi, etc.) Kaggle Your own life! (FitBit, your bank) 21 / 27

28. Dataset Sources The Open Data movement (Bixi, etc.) Kaggle Your own life! (FitBit, your bank) The Internet! 22 / 27

29. Project Ideas Forecast Bixi usage 23 / 27

30. Project Ideas Forecast Bixi usage Cat vs. dog detector 24 / 27

31. Project Ideas Forecast Bixi usage Cat vs. dog detector Automated categorization of credit card bill items 25 / 27

32. After your rst project Getting better at Python: https://www.youtube.com/playlist?list=PLu5kChXP9Hipqr1gIROxGlvb3YVadOjUB (14 videos currently) Learning the basics of classes and object-oriented programming: http://www.greenteapress.com/thinkpython/html/thinkpython016.html (and next two chapters) The matrix algebra you need for machine learning: http://parrt.cs.usfca.edu/doc/matrix-calculus/ Scaling with Dask and Dask-ml and Dask-Kubernetes Learning the basics of SQL 26 / 27

33. Thank you! Slides and notebooks: http://christianhudon.name/talks/#DSDT-2018-06 Tools Trying things out: Jupyter Lab Numerical computations: Numpy Data Exploration: Pandas Classical machine learning: Scikit-learn (and SpaCy) Deep learning: Pytorch (and Skorch) Visualization: Pandas and Seaborn More Visualization: HoloViews (or Altair) 27 / 27

34. Topic # 2 AI to predict and prevent bugs in code Data Science | Design | Technology 1 Ubisoft La Forge

35.

36. • •

37. Fixes Fixes Fixes Fixes Fixes Fixes Fix

38. • • •

42. • • •

44.

45.