talk-data.com

Generating datasets for semantic segmentation can be time-intensive. Learn how to use Blender’s Python API to create diverse and realistic synthetic data with automated labels, saving time and improving model performance. Preview the topics to be discussed in this Medium post.

Generating datasets for semantic segmentation can be time-intensive. Learn how to use Blender's Python API to create diverse and realistic synthetic data with automated labels, saving time and improving model performance. Preview the topics to be discussed in this Medium post.

Join us for a quick update on the Elderly Action Recognition (EAR) Challenge, part of the Computer Vision for Smalls (CV4Smalls) Workshop at the WACV 2025 conference! This challenge focuses on advancing research in Activity of Daily Living (ADL) recognition, particularly within the elderly population, a domain with profound societal implications. Participants will employ transfer learning techniques with any architecture or model they want to use. For example, starting with a general human action recognition benchmark and fine-tuning models on a subset of data tailored to elderly-specific activities.

The future of education lies in personalized and scalable solutions, especially in fields like computer engineering where complex concepts often challenge students. This talk introduces Lumina (AI Teaching Assistant), a cutting-edge agentic system designed to revolutionize programming education through its innovative architecture and teaching strategies. Built using OpenAI API, LangChain, RAG, and ChromaDB, Lumina employs an agentic, multi-modal framework that dynamically integrates course materials, technical documentation, and pedagogical strategies into an adaptive knowledge-driven system. Its unique “Knowledge Components” approach decomposes programming concepts into interconnected teachable units, enabling proficiency-based learning and dynamic problem-solving guidance. Attendees will discover how Lumina’s agentic architecture enhances engagement, fosters critical thinking, and improves concept mastery, paving the way for scalable AI-driven educational solutions.

Register for the Zoom!

Date and Time Jan 30, 2025 at 10 AM Pacific

Swimming Upstream: Using Machine Vision to Create Sustainable Practices in Fisheries of the Future

Fishing vessels are on track to generate 10 million hours of video footage annually, creating a massive machine learning operations challenge. At AI.Fish, we are building an end-to-end system enabling non-technical users to harness AI for catch monitoring and classification both on-board and in the cloud. This talk explores our journey in building these approachable systems and working toward answering an old question: How many fish are in the ocean?

About the Speaker

Orvis Evans is a Software Engineer at AI.Fish, where he co-architects ML-Ops pipelines and develops intuitive interfaces that make machine vision accessible to non-technical users. Drawing from his background in building interactive systems, he builds front-end applications and APIs that enable fisheries to process thousands of hours of footage without machine learning expertise.

Scaling Semantic Segmentation with Blender

Generating datasets for semantic segmentation can be time-intensive. Learn how to use Blender’s Python API to create diverse and realistic synthetic data with automated labels, saving time and improving model performance. Preview the topics to be discussed in this Medium post.

About the Speaker Vincent Vandenbussche has a PhD in Physics, is an author, and Machine Learning Engineer with 10 years of experience in software engineering and machine learning.

WACV 2025 - Elderly Action Recognition Challenge

Join us for a quick update on the Elderly Action Recognition (EAR) Challenge, part of the Computer Vision for Smalls (CV4Smalls) Workshop at the WACV 2025 conference!

This challenge focuses on advancing research in Activity of Daily Living (ADL) recognition, particularly within the elderly population, a domain with profound societal implications. Participants will employ transfer learning techniques with any architecture or model they want to use. For example, starting with a general human action recognition benchmark and fine-tuning models on a subset of data tailored to elderly-specific activities.

Sign up for the EAR challenge and learn more.

About the Speaker

Paula Ramos, PhD is a Senior DevRel and Applied AI Research Advocate at Voxel51.

Transforming Programming Ed: An AI-Powered Teaching Assistant for Scalable and Adaptive Learning

The future of education lies in personalized and scalable solutions, especially in fields like computer engineering where complex concepts often challenge students. This talk introduces Lumina (AI Teaching Assistant), a cutting-edge agentic system designed to revolutionize programming education through its innovative architecture and teaching strategies. Built using OpenAI API, LangChain, RAG, and ChromaDB, Lumina employs an agentic, multi-modal framework that dynamically integrates course materials, technical documentation, and pedagogical strategies into an adaptive knowledge-driven system. Its unique “Knowledge Components” approach decomposes programming concepts into interconnected teachable units, enabling proficiency-based learning and dynamic problem-solving guidance. Attendees will discover how Lumina’s agentic architecture enhances engagement, fosters critical thinking, and improves concept mastery, paving the way for scalable AI-driven educational solutions.

About the Speaker

Nittin Murthi Dhekshinamoorthy is a computer engineering student and researcher at the University of Illinois Urbana-Champaign with a strong focus on advancing artificial intelligence to solve real-world challenges in education and technology. He is the creator of an AI agent-based Teaching Assistant, leveraging cutting-edge frameworks to provide scalable, adaptive learning solutions, and has contributed to diverse, impactful projects, including natural language-to-SQL systems and deep learning models for clinical image segmentation.

Register for the Zoom!

Date and Time Jan 30, 2025 at 10 AM Pacific

Swimming Upstream: Using Machine Vision to Create Sustainable Practices in Fisheries of the Future

Fishing vessels are on track to generate 10 million hours of video footage annually, creating a massive machine learning operations challenge. At AI.Fish, we are building an end-to-end system enabling non-technical users to harness AI for catch monitoring and classification both on-board and in the cloud. This talk explores our journey in building these approachable systems and working toward answering an old question: How many fish are in the ocean?

About the Speaker

Orvis Evans is a Software Engineer at AI.Fish, where he co-architects ML-Ops pipelines and develops intuitive interfaces that make machine vision accessible to non-technical users. Drawing from his background in building interactive systems, he builds front-end applications and APIs that enable fisheries to process thousands of hours of footage without machine learning expertise.

Scaling Semantic Segmentation with Blender

Generating datasets for semantic segmentation can be time-intensive. Learn how to use Blender’s Python API to create diverse and realistic synthetic data with automated labels, saving time and improving model performance. Preview the topics to be discussed in this Medium post.

About the Speaker Vincent Vandenbussche has a PhD in Physics, is an author, and Machine Learning Engineer with 10 years of experience in software engineering and machine learning.

WACV 2025 - Elderly Action Recognition Challenge

Join us for a quick update on the Elderly Action Recognition (EAR) Challenge, part of the Computer Vision for Smalls (CV4Smalls) Workshop at the WACV 2025 conference!

This challenge focuses on advancing research in Activity of Daily Living (ADL) recognition, particularly within the elderly population, a domain with profound societal implications. Participants will employ transfer learning techniques with any architecture or model they want to use. For example, starting with a general human action recognition benchmark and fine-tuning models on a subset of data tailored to elderly-specific activities.

Sign up for the EAR challenge and learn more.

About the Speaker

Paula Ramos, PhD is a Senior DevRel and Applied AI Research Advocate at Voxel51.

Transforming Programming Ed: An AI-Powered Teaching Assistant for Scalable and Adaptive Learning

The future of education lies in personalized and scalable solutions, especially in fields like computer engineering where complex concepts often challenge students. This talk introduces Lumina (AI Teaching Assistant), a cutting-edge agentic system designed to revolutionize programming education through its innovative architecture and teaching strategies. Built using OpenAI API, LangChain, RAG, and ChromaDB, Lumina employs an agentic, multi-modal framework that dynamically integrates course materials, technical documentation, and pedagogical strategies into an adaptive knowledge-driven system. Its unique “Knowledge Components” approach decomposes programming concepts into interconnected teachable units, enabling proficiency-based learning and dynamic problem-solving guidance. Attendees will discover how Lumina’s agentic architecture enhances engagement, fosters critical thinking, and improves concept mastery, paving the way for scalable AI-driven educational solutions.

About the Speaker

Nittin Murthi Dhekshinamoorthy is a computer engineering student and researcher at the University of Illinois Urbana-Champaign with a strong focus on advancing artificial intelligence to solve real-world challenges in education and technology. He is the creator of an AI agent-based Teaching Assistant, leveraging cutting-edge frameworks to provide scalable, adaptive learning solutions, and has contributed to diverse, impactful projects, including natural language-to-SQL systems and deep learning models for clinical image segmentation.

Register for the Zoom!

Date and Time Jan 30, 2025 at 10 AM Pacific

Swimming Upstream: Using Machine Vision to Create Sustainable Practices in Fisheries of the Future

Fishing vessels are on track to generate 10 million hours of video footage annually, creating a massive machine learning operations challenge. At AI.Fish, we are building an end-to-end system enabling non-technical users to harness AI for catch monitoring and classification both on-board and in the cloud. This talk explores our journey in building these approachable systems and working toward answering an old question: How many fish are in the ocean?

About the Speaker

Orvis Evans is a Software Engineer at AI.Fish, where he co-architects ML-Ops pipelines and develops intuitive interfaces that make machine vision accessible to non-technical users. Drawing from his background in building interactive systems, he builds front-end applications and APIs that enable fisheries to process thousands of hours of footage without machine learning expertise.

Scaling Semantic Segmentation with Blender

Generating datasets for semantic segmentation can be time-intensive. Learn how to use Blender’s Python API to create diverse and realistic synthetic data with automated labels, saving time and improving model performance. Preview the topics to be discussed in this Medium post.

About the Speaker Vincent Vandenbussche has a PhD in Physics, is an author, and Machine Learning Engineer with 10 years of experience in software engineering and machine learning.

WACV 2025 - Elderly Action Recognition Challenge

Join us for a quick update on the Elderly Action Recognition (EAR) Challenge, part of the Computer Vision for Smalls (CV4Smalls) Workshop at the WACV 2025 conference!

This challenge focuses on advancing research in Activity of Daily Living (ADL) recognition, particularly within the elderly population, a domain with profound societal implications. Participants will employ transfer learning techniques with any architecture or model they want to use. For example, starting with a general human action recognition benchmark and fine-tuning models on a subset of data tailored to elderly-specific activities.

Sign up for the EAR challenge and learn more.

About the Speaker

Paula Ramos, PhD is a Senior DevRel and Applied AI Research Advocate at Voxel51.

Transforming Programming Ed: An AI-Powered Teaching Assistant for Scalable and Adaptive Learning

The future of education lies in personalized and scalable solutions, especially in fields like computer engineering where complex concepts often challenge students. This talk introduces Lumina (AI Teaching Assistant), a cutting-edge agentic system designed to revolutionize programming education through its innovative architecture and teaching strategies. Built using OpenAI API, LangChain, RAG, and ChromaDB, Lumina employs an agentic, multi-modal framework that dynamically integrates course materials, technical documentation, and pedagogical strategies into an adaptive knowledge-driven system. Its unique “Knowledge Components” approach decomposes programming concepts into interconnected teachable units, enabling proficiency-based learning and dynamic problem-solving guidance. Attendees will discover how Lumina’s agentic architecture enhances engagement, fosters critical thinking, and improves concept mastery, paving the way for scalable AI-driven educational solutions.

About the Speaker

Nittin Murthi Dhekshinamoorthy is a computer engineering student and researcher at the University of Illinois Urbana-Champaign with a strong focus on advancing artificial intelligence to solve real-world challenges in education and technology. He is the creator of an AI agent-based Teaching Assistant, leveraging cutting-edge frameworks to provide scalable, adaptive learning solutions, and has contributed to diverse, impactful projects, including natural language-to-SQL systems and deep learning models for clinical image segmentation.

Fishing vessels are on track to generate 10 million hours of video footage annually, creating a massive machine learning operations challenge. At AI.Fish, we are building an end-to-end system enabling non-technical users to harness AI for catch monitoring and classification both on-board and in the cloud. This talk explores our journey in building these approachable systems and working toward answering an old question: How many fish are in the ocean?

Register for the Zoom:

https://voxel51.com/computer-vision-events/ai-machine-learning-computer-vision-meetup-oct-10-2024/

How Renault Leveraged Machine Learning to Scale Electric Vehicle Sales

In 2019, Renault sought a scalable solution to estimate the cost of home charging station installations for electric vehicle buyers. A machine learning solution using satellite images and a shortest-path algorithm was developed to automate this process. Despite challenges, the optimized solution was deployed as a cloud-based API, enabling Renault to scale their EV sales from 50,000 in 2019 to over 220,000 in 2022.

About the Speaker

With a PhD in Physics, Vincent Vandenbussche has over a decade of experience deploying scalable machine learning solutions for leading companies like Renault and Chanel. He is also passionate about sharing his expertise through Medium posts and his book, The Regularization Cookbook.

RGB-X Model Development: Exploring Four Channel ML Workflows

Machine Learning is rapidly becoming multimodal. With many models in Computer Vision expanding to areas like vision and 3D, one area that has also quietly been advancing rapidly is RGB-X data, such as infrared, depth, or normals. In this talk we will cover some of the leading models in this exploding field of Visual AI and show some best practices on how to work with these complex data formats!

About the Speaker

Daniel Gural is a seasoned Machine Learning Evangelist with a strong passion for empowering Data Scientists and ML Engineers to unlock the full potential of their data. Currently serving as a valuable member of Voxel51, he takes a leading role in efforts to bridge the gap between practitioners and the necessary tools, enabling them to achieve exceptional outcomes. Daniel’s extensive experience in teaching and developing within the ML field has fueled his commitment to democratizing high-quality AI workflows for a wider audience.

Elasticsearch is for the Birds: Identifying Feathered Friend Embedding Images as Vector Similarity Search

Search no longer has to be a traditional term / frequency-inverse document frequency slog. The current trend of machine learning and models has opened another dimension for search, quite literally. In this talk we’ll cover “classic” search and its inherent limitations, what a model is and how you can use it.

Next, we’ll look at how to perform vector search or hybrid search using images of birds in Elasticsearch, generate embeddings from images and then use the techniques we learned to propose the most probable similar images after uploading our own image. We’ll close the talk with an overview of various enhancements to increase the performance and usability of your searches.

About the Speaker

Justin Castilla is a Senior Developer Advocate at Elastic based in Seattle. His main focus is education and developer empowerment, and enjoys sharing knowledge and learning experiences with everyone.

Register for the Zoom:

https://voxel51.com/computer-vision-events/ai-machine-learning-computer-vision-meetup-oct-10-2024/

How Renault Leveraged Machine Learning to Scale Electric Vehicle Sales

In 2019, Renault sought a scalable solution to estimate the cost of home charging station installations for electric vehicle buyers. A machine learning solution using satellite images and a shortest-path algorithm was developed to automate this process. Despite challenges, the optimized solution was deployed as a cloud-based API, enabling Renault to scale their EV sales from 50,000 in 2019 to over 220,000 in 2022.

About the Speaker

With a PhD in Physics, Vincent Vandenbussche has over a decade of experience deploying scalable machine learning solutions for leading companies like Renault and Chanel. He is also passionate about sharing his expertise through Medium posts and his book, The Regularization Cookbook.

RGB-X Model Development: Exploring Four Channel ML Workflows

Machine Learning is rapidly becoming multimodal. With many models in Computer Vision expanding to areas like vision and 3D, one area that has also quietly been advancing rapidly is RGB-X data, such as infrared, depth, or normals. In this talk we will cover some of the leading models in this exploding field of Visual AI and show some best practices on how to work with these complex data formats!

About the Speaker

Daniel Gural is a seasoned Machine Learning Evangelist with a strong passion for empowering Data Scientists and ML Engineers to unlock the full potential of their data. Currently serving as a valuable member of Voxel51, he takes a leading role in efforts to bridge the gap between practitioners and the necessary tools, enabling them to achieve exceptional outcomes. Daniel’s extensive experience in teaching and developing within the ML field has fueled his commitment to democratizing high-quality AI workflows for a wider audience.

Elasticsearch is for the Birds: Identifying Feathered Friend Embedding Images as Vector Similarity Search

Search no longer has to be a traditional term / frequency-inverse document frequency slog. The current trend of machine learning and models has opened another dimension for search, quite literally. In this talk we’ll cover “classic” search and its inherent limitations, what a model is and how you can use it.

Next, we’ll look at how to perform vector search or hybrid search using images of birds in Elasticsearch, generate embeddings from images and then use the techniques we learned to propose the most probable similar images after uploading our own image. We’ll close the talk with an overview of various enhancements to increase the performance and usability of your searches.

About the Speaker

Justin Castilla is a Senior Developer Advocate at Elastic based in Seattle. His main focus is education and developer empowerment, and enjoys sharing knowledge and learning experiences with everyone.

Register for the Zoom:

https://voxel51.com/computer-vision-events/ai-machine-learning-computer-vision-meetup-oct-10-2024/

How Renault Leveraged Machine Learning to Scale Electric Vehicle Sales

In 2019, Renault sought a scalable solution to estimate the cost of home charging station installations for electric vehicle buyers. A machine learning solution using satellite images and a shortest-path algorithm was developed to automate this process. Despite challenges, the optimized solution was deployed as a cloud-based API, enabling Renault to scale their EV sales from 50,000 in 2019 to over 220,000 in 2022.

About the Speaker

With a PhD in Physics, Vincent Vandenbussche has over a decade of experience deploying scalable machine learning solutions for leading companies like Renault and Chanel. He is also passionate about sharing his expertise through Medium posts and his book, The Regularization Cookbook.

RGB-X Model Development: Exploring Four Channel ML Workflows

Machine Learning is rapidly becoming multimodal. With many models in Computer Vision expanding to areas like vision and 3D, one area that has also quietly been advancing rapidly is RGB-X data, such as infrared, depth, or normals. In this talk we will cover some of the leading models in this exploding field of Visual AI and show some best practices on how to work with these complex data formats!

About the Speaker

Daniel Gural is a seasoned Machine Learning Evangelist with a strong passion for empowering Data Scientists and ML Engineers to unlock the full potential of their data. Currently serving as a valuable member of Voxel51, he takes a leading role in efforts to bridge the gap between practitioners and the necessary tools, enabling them to achieve exceptional outcomes. Daniel’s extensive experience in teaching and developing within the ML field has fueled his commitment to democratizing high-quality AI workflows for a wider audience.

Elasticsearch is for the Birds: Identifying Feathered Friend Embedding Images as Vector Similarity Search

Search no longer has to be a traditional term / frequency-inverse document frequency slog. The current trend of machine learning and models has opened another dimension for search, quite literally. In this talk we’ll cover “classic” search and its inherent limitations, what a model is and how you can use it.

Next, we’ll look at how to perform vector search or hybrid search using images of birds in Elasticsearch, generate embeddings from images and then use the techniques we learned to propose the most probable similar images after uploading our own image. We’ll close the talk with an overview of various enhancements to increase the performance and usability of your searches.

About the Speaker

Justin Castilla is a Senior Developer Advocate at Elastic based in Seattle. His main focus is education and developer empowerment, and enjoys sharing knowledge and learning experiences with everyone.

Register for the Zoom:

https://voxel51.com/computer-vision-events/ai-machine-learning-computer-vision-meetup-oct-10-2024/

How Renault Leveraged Machine Learning to Scale Electric Vehicle Sales

In 2019, Renault sought a scalable solution to estimate the cost of home charging station installations for electric vehicle buyers. A machine learning solution using satellite images and a shortest-path algorithm was developed to automate this process. Despite challenges, the optimized solution was deployed as a cloud-based API, enabling Renault to scale their EV sales from 50,000 in 2019 to over 220,000 in 2022.

About the Speaker

With a PhD in Physics, Vincent Vandenbussche has over a decade of experience deploying scalable machine learning solutions for leading companies like Renault and Chanel. He is also passionate about sharing his expertise through Medium posts and his book, The Regularization Cookbook.

RGB-X Model Development: Exploring Four Channel ML Workflows

Machine Learning is rapidly becoming multimodal. With many models in Computer Vision expanding to areas like vision and 3D, one area that has also quietly been advancing rapidly is RGB-X data, such as infrared, depth, or normals. In this talk we will cover some of the leading models in this exploding field of Visual AI and show some best practices on how to work with these complex data formats!

About the Speaker

Daniel Gural is a seasoned Machine Learning Evangelist with a strong passion for empowering Data Scientists and ML Engineers to unlock the full potential of their data. Currently serving as a valuable member of Voxel51, he takes a leading role in efforts to bridge the gap between practitioners and the necessary tools, enabling them to achieve exceptional outcomes. Daniel’s extensive experience in teaching and developing within the ML field has fueled his commitment to democratizing high-quality AI workflows for a wider audience.

Elasticsearch is for the Birds: Identifying Feathered Friend Embedding Images as Vector Similarity Search

Search no longer has to be a traditional term / frequency-inverse document frequency slog. The current trend of machine learning and models has opened another dimension for search, quite literally. In this talk we’ll cover “classic” search and its inherent limitations, what a model is and how you can use it.

Next, we’ll look at how to perform vector search or hybrid search using images of birds in Elasticsearch, generate embeddings from images and then use the techniques we learned to propose the most probable similar images after uploading our own image. We’ll close the talk with an overview of various enhancements to increase the performance and usability of your searches.

About the Speaker

Justin Castilla is a Senior Developer Advocate at Elastic based in Seattle. His main focus is education and developer empowerment, and enjoys sharing knowledge and learning experiences with everyone.