What we’re about
🖖 This virtual group is for data scientists, machine learning engineers, and open source enthusiasts who want to expand their knowledge of computer vision and complementary technologies. Every month we’ll bring you two diverse speakers working at the cutting edge of computer vision.
- Are you interested in speaking at a future Meetup?
- Is your company interested in sponsoring a Meetup?
Contact the Meetup organizers!
This Meetup is sponsored by Voxel51, the lead maintainers of the open source FiftyOne computer vision toolset. To learn more about FiftyOne, visit the project page on GitHub: https://github.com/voxel51/fiftyone
📣 Past Speakers
* Sage Elliott at Union.ai
* Michael Wornow at Microsoft
* Argo Saakyan at Veryfi
* Justin Trugman at Softwaretesting.ai
* Johannes Flotzinger at Universität der Bundeswehr München
* Harpreet Sahota at Deci,ai
* Nora Gourmelon at Friedrich-Alexander-Universität Erlangen-Nürnberg
* Reid Pryzant at Microsoft
* David Mezzetti at NeuML
* Chaitanya Mitash at Amazon Robotics
* Fan Wang at Amazon Robotics
* Mani Nambi at Amazon Robotics
* Joy Timmermans at Secury360
* Eduardo Alvarez at Intel
* Minye Wu at KU Leuven
* Jizhizi Li at University of Sydney
* Raz Petel at SightX
* Karttikeya Mangalam at UC Berkeley
* Dolev Ofri-Amar at Weizmann Institute of Science
* Roushanak Rahmat, PhD
* Folefac Martins
* Zhixi Cai at Monash University
* Filip Haltmayer at Zilliz
* Stephanie Fu at MIT
* Shobhita Sundaram at MIT
* Netanel Tamir at Weizmann Institute of Science
* Glenn Jocher at Ultralytics
* Michal Geyer at Weizmann Institute of Science
* Narek Tumanya at Weizmann Institute of Science
* Jerome Pasquero at Sama
* Eric Zimmermann at Sama
* Victor Anton at Wildlife.ai
* Shashwat Srivastava at Opendoor
* Eugene Khvedchenia at Deci.ai
* Hila Chefer at Tel-Aviv University
* Zhuo Wu at Intel
* Chuan Guo at University of Alberta
* Dhruv Batra Meta & Georgia Tech
* Benjamin Lahner at MIT
* Jiajing Chen at Syracuse University
* Soumik Rakshit at Weights & Biases
* Jiajing Chen at Syracuse University
* Paula Ramos, PhD at Intel
* Vishal Rajput at Skybase
* Cameron Wolfe at Alegion/Rice University
* Julien Simon at Hugging Face
* Kris Kitani at Carnegie Mellon University
* Anna Kogan at OpenCV.ai
* Kacper Łukawski at Qdrant
* Sri Anumakonda
* Tarik Hammadou at NVIDIA
* Zain Hasan at Weaviate
* Jai Chopra at LanceDB
* Sven Dickinson at University of Toronto & Samsung
* Nalini Singh at MIT
📚 Resources
* YouTube Playlist of previous Meetups
* Recap blogs including Q&A and speaker resource links
Sponsors
See all
Voxel51Administration, promotion, giveaways and charitable contributions.
Voxel51Administration, promotion, giveaways and charitable contributions.- Voxel51Administration, promotion, giveaways and charitable contributions.
- Voxel51Administration, promotion, giveaways and charitable contributions.
- Voxel51Administration, promotion, giveaways and charitable contributions.
- Voxel51Administration, promotion, giveaways and charitable contributions.
Upcoming events (4+)
See all- Network event109 attendees from 16 groups hostingApril 5-6: FREE 2-Day Deep Learning Fundamentals NVIDIA DLI Certification CourseLink visible for attendees
Register for the FREE 2 day training course.
Deep Learning Fundamentals with PyTorch and FiftyOne
NVIDIA DLI Certification Workshop for AcademiaApril 5-6, 2025 | 10 AM – 5 PM CET
About the Workshop
Note: This course is only available to university students, researchers and teaching staff. A valid university email address is required to obtain a certificate.
This two day workshop is an extension of NVIDIA’s DLI Deep Learning Fundamentals Course.
Learn to implement neural networks for image classification from scratch using PyTorch. Learn about stochastic gradient descent, multilayer perceptrons, convolutional neural networks, and transformers. We will explore data augmentation, workflow management, and dataset curation using FiftyOne, a powerful open source tool for image dataset curation.
On the first day we will focus on building and training neural networks with PyTorch.
On the second day we will focus on visual dataset curation with FiftyOne and iterative improvement of image classification models.
Instructor
Antonio Rueda-Toicen, an AI Engineer in Berlin, has extensive experience in deploying machine learning models and has taught over 300 professionals. He is currently a Research Scientist at the Hasso Plattner Institute. Antonio is a certified instructor of deep learning and diffusion models at NVIDIA’s Deep Learning Institute.
Prerequisites
- Programming Prerequisites: Python fundamentals
- Mathematics Prerequisites: Statistics and probability, linear algebra, calculus
Technologies Used in the Workshop
- Python
- Jupyter
- PyTorch
- Pandas
- FiftyOne
- Google Colab
- Github Codespaces
Assessment Type
Skills-based coding assessments evaluate students’ ability to train a deep learning model to classify images with high accuracy.
Certificate
Upon successful completion of the coding assessment, participants will receive an NVIDIA DLI certificate to recognize their subject matter competency and support professional career growth.
Hardware Requirements
Desktop or laptop computer capable of running the latest version of Chrome or Firefox. Each participant will be provided with dedicated access to a fully configured, GPU-accelerated server in the cloud.
Language
English
- Network event10 attendees from 16 groups hostingApril 8 - Multi-label Classification with Binary Cross Entropy WorkshopLink visible for attendees
When and Where
- April 8, 2025
- 6:30 PM to 8:30 PM CET | 9:30 AM to 11:30 AM Pacific
- Workshops are delivered over Zoom
About the Workshop
Join us for a 12-part, hands-on series that teaches you how to work with images, build and train models, and explore tasks like image classification, segmentation, object detection, and image generation. Each session combines straightforward explanations with practical coding in PyTorch and FiftyOne, allowing you to learn core skills in computer vision and apply them to real-world tasks.
In this session, we’ll focus on multi-label classification for real-world imagery. Build a model that identifies multiple environmental labels from Amazon satellite images, applying binary cross-entropy for training and analyzing predictions with FiftyOne.
These are hands-on maker workshops that make use of GitHub Codespaces, Kaggle notebooks, and Google Colab environments, so no local installation is required (though you are welcome to work locally if preferred!)
Workshop Resources
You can find the workshop materials in this GitHub repository.
About the Instructor
Antonio Rueda-Toicen, an AI Engineer in Berlin, has extensive experience in deploying machine learning models and has taught over 300 professionals. He is currently a Research Scientist at the Hasso Plattner Institute. Since 2019, he has organized the Berlin Computer Vision Group and taught at Berlin’s Data Science Retreat. He specializes in computer vision, cloud technologies, and machine learning. Antonio is also a certified instructor of deep learning and diffusion models in NVIDIA’s Deep Learning Institute.
- April 10 - Chicago AI, ML, and Computer Vision MeetupChicago Meetup, chicago, IL
Pre-Registration is mandatory for building security.
Register to reserve your spot.
When and Where
April 10, 2025 | 5:30 – 8:30 PM
10 S Riverside Plaza Suite 800
Chicago, IL 60606Note: The main entrance to the building is under construction. Guests must enter the building on the corner of Monroe and Canal.
GenAI on Data Intelligence Platforms
Hear first-hand how data, analytics, and AI are innovating every business industry. Ari will lead a lively discussion on the latest in AI: data intelligence platform on a data lakehouse architecture, the importance of collaboration among humans and their data, and a demo on creating a fully governed LLM chatbot in under a minute!
About the Speaker
Ari Kaplan is the Head of Evangelism at Databricks and is a renowned data science and AI leader whose analytical innovations helped inspire “Moneyball.” Former head of Chicago Cubs analytics, and Global AI Evangelist at DataRobot. Named among DataIQ’s top 20 AI influencers (2024) and Caltech Alumni of the Decade. Serves on Intel’s AI Board of Advisors.
ReAct Meets Reinforcement Learning: A Discussion on Reward Shaping for Adaptive Agent Design
Agent architectures are evolving toward a reinforcement learning mindset. The “observe-plan-act” ReAct paradigm, for instance, matches the RL paradigm almost exactly. This convergence naturally raises the question: could RL techniques actually improve our agentic systems? In this talk, I’ll explore the answer to that question. I’ll show how to leverage external signals as an implicit reward function, thereby allowing agents to autonomously learn from their environment. I’ll also discuss how inverse reinforcement learning (IRL) techniques can infer a reward function implicitly from observing human expert demonstrations. Using a human-in-the-loop tech-recruiting platform as our guide, we’ll see how these techniques can fine-tune agent behavior through both external cues and expert feedback.
About the Speaker
Jack Blandin is the founder of Lambda League, a tech recruiting startup focused on simplifying the tech hiring process. Before launching Lambda League in 2023, he served as VP of Machine Learning at Fi and held machine learning leadership and IC roles at Wayfair, Trunk Club, and GoHealth. Jack holds a PhD in Computer Science from UIC, where his research focused on reinforcement learning, inverse reinforcement learning, and algorithmic fairness.
Multimodal RAG and Contextual Retrieval with Pinecone and Anthropic
Building multimodal RAG applications can be tricky, especially for video search applications. When working with recorded presentations, information can exist on screen and in spoken audio, which requires models and processing techniques that take advantage of this asymmetry.
In this talk, Arjun Patel (Developer Advocate at Pinecone) will demo an application built with Pinecone’s vector database and Claude models that allows for retrieval augmented generation over webinar videos.
The key trick is applying Anthropic’s technique of contextual retrieval to preprocess and enrich the video data into text for semantic search, along with a multimodal RAG step with Claude. The talk will cover how and why contextual retrieval works, preprocessing video data into image text pairs, and leveraging the multimodal nature of Claude to further refine response quality.
About the Speaker
As a Developer Advocate at Pinecone, Arjun Patel creates educational content on vector databases, retrieval augmented generation, and semantic search across various platforms. With a BA in Statistics from the University of Chicago, he brings extensive expertise in natural language processing, deep learning, and large language models to his role.
His professional journey includes data science projects at Speeko and Appen, where he contributed to AI-driven speech coaching and AI-generated content detection, respectively. Beyond his professional pursuits, Arjun is an accomplished origami artist, blending analytical thinking with creative expression in his decade-long hobby.
Business of AI
The talk will focus on the importance of clearly defining a specific problem and a use case, how to quantify the potential benefits of an AI solution in terms of measurable outcomes, evaluating technical feasibility in terms of technical challenges and limitations of implementing an AI solution, and envisioning the future of enterprise AI.
About the Speaker
Milica Cvetkovic is an AI engineer and consultant driving the development and deployment of production-ready AI systems for diverse organizations. Her expertise spans custom machine learning, generative AI, and AI operationalization. With degrees in mathematics and statistics, she possesses a decade of experience in education and edtech, including curriculum design and machine learning instruction for technical and non-technical audiences. Prior to Google, Milica held a data scientist role in biotechnology and has a proven track record of advising startups, demonstrating a deep understanding of AI’s practical application.
- Network event6 attendees from 16 groups hostingApril 15 - Interpretability in Computer Vision: CAM & Grad-CAM WorkshopLink visible for attendees
When and Where
- April 15, 2025
- 6:30 PM to 8:30 PM CET | 9:30 AM to 11:30 AM Pacific
- Workshops are delivered over Zoom
About the Workshop
Join us for a 12-part, hands-on series that teaches you how to work with images, build and train models, and explore tasks like image classification, segmentation, object detection, and image generation. Each session combines straightforward explanations with practical coding in PyTorch and FiftyOne, allowing you to learn core skills in computer vision and apply them to real-world tasks.
In this session, we’ll learn interpretability techniques such as Class Activation Mapping and Grad-CAM. Build a model to analyze predictions and visualize important image regions with FiftyOne.
These are hands-on maker workshops that make use of GitHub Codespaces, Kaggle notebooks, and Google Colab environments, so no local installation is required (though you are welcome to work locally if preferred!)
Workshop Resources
You can find the workshop materials in this GitHub repository.
About the Instructor
Antonio Rueda-Toicen, an AI Engineer in Berlin, has extensive experience in deploying machine learning models and has taught over 300 professionals. He is currently a Research Scientist at the Hasso Plattner Institute. Since 2019, he has organized the Berlin Computer Vision Group and taught at Berlin’s Data Science Retreat. He specializes in computer vision, cloud technologies, and machine learning. Antonio is also a certified instructor of deep learning and diffusion models in NVIDIA’s Deep Learning Institute.
Past events (100)
See all- Network event45 attendees from 16 groups hostingApril 1 - Training Techniques for Convolutional Networks WorkshopThis event has passed
