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?

Hello plant developers and language twisters!

The 24th Prague PyData meetup will take place at CreativeDock. As usual, the talks will start at 18:30 but we encourage you to come as soon as 18:00 to enjoy the opportunity to socialise and refresh yourselves (which you can continue doing during the break and after the talks).

Our main goal is to build the community around Python and data and make it welcoming to people of various skills and experience levels.

⚡ If you are interested in giving a lightning talk (up to 5 minutes to present an idea, tool or results related at least to some degree to Python and/or data), please contact us before or during the event.

📢 Processing problematic plants with python Adam Hruška Weed detection techniques are essential in modern precision agriculture, where accurately detecting and identifying species can lead to effective crop yields and sustainable resource use. Python has emerged as a powerful tool in this domain, providing versatile libraries and frameworks for data gathering, processing, and model training. By leveraging Python's capabilities, we can efficiently manage large-scale datasets, preprocess images, and potentially automate the annotation process, streamlining the development of machine learning models for weed detection. The integration of computer vision and machine learning tools, including OpenCV, scikit-image, and TensorFlow, serves as a basis for creation of models capable of distinguishing between crops and weed species with high precision. As a result, Python-driven weed detection models offer a promising path toward improved crop health, resource conservation, and sustainable farming practices.

📢 Beyond Accuracy: Engineering EU-Compliant LLM Systems Soheyla Mirshahi, Jan Kryštůfek (CreativeDock) Building production-ready LLM systems is challenging, but designing solutions that comply with the EU AI Act introduces layers of complexity far beyond prompt engineering and API integration. This talk takes you through the journey of creating a compliant LLM system for one of the most demanding domains: AI-driven recruitment. We share our journey from a naive PoC to a production ready solution under the AI Act. Through this case study, we will explore:

• Compliance-first approach: Designing LLM systems to meet security, legal and ethical standards from the ground up
• Taming LLM in Python: Using prompts, function calls and scoring model to get results in structured and auditable form
• Evaluation: Testing on synthetic data, human evaluation and user feedback
• Numbers: Statictics from production and percieved benefits

Attendees will leave equipped with practical tools, architectural patterns for building LLM systems that meet both engineering and regulatory challenges, supported by real-world example.

The venue will open at 6.00pm but the intro won't take place sooner than at 6:30pm. We should enter via the Expo 58 gallery (in the middle floor of the 3-floor building). There will be food and drinks available, 🤗 sponsors.

Please, RSVP here. See you soon, PyData Prague team