Seattle AI, Machine Learning and Data Science Meetup

Zoom: https://voxel51.com/computer-vision-events/fiftyone-workshop-sept-27/

Want greater visibility into the quality of your computer vision datasets and models? Then join Dan Gural, machine learning engineer at Voxel51, for this free 90 minute, hands-on workshop to learn how to leverage the open source FiftyOne computer vision toolset.

In the first part of the workshop we’ll cover:

• FiftyOne Basics (terms, architecture, installation, and general usage)
• An overview of useful workflows to explore, understand, and curate your data
• How FiftyOne represents and semantically slices unstructured computer vision data

The second half will be a hands-on introduction to FiftyOne, where you will learn how to:

• Load datasets from the FiftyOne Dataset Zoo
• Navigate the FiftyOne App
• Programmatically inspect attributes of a dataset
• Add new sample and custom attributes to a dataset
• Generate and evaluate model predictions
• Save insightful views into the data

Prerequisites are a working knowledge of python and basic computer vision. All attendees will get access to the tutorials, videos, and code examples used in the workshop.

https://voxel51.com/computer-vision-events/october-ai-machine-learning-data-science-meetup/

Glacier Monitoring with Computer Vision Models
The temporal variability of marine-terminating glacier front positions provides valuable information on the state of the glaciers. Therefore, the position of these fronts is an important parameter influencing the accuracy of climate models. To obtain the position, satellite imagery has traditionally been analyzed by hand. As the amount of satellite imagery and the need for accurate climate models is increasing, deep learning techniques are applied to extract the glacier front position from satellite images. In this talk, state-of-the-art models for this purpose will be discussed.

Nora Gourmelon is a PhD candidate in Computer Science at the Friedrich-Alexander-Universität Erlangen-Nürnberg working on AI for Earth. Her main focus lies on the segmentation of glacier calving fronts in Synthetic Aperture Radar (SAR) satellite imagery.

Automatic Prompt Optimization with “Gradient Descent” and Beam Search
Large Language Models (LLMs) have shown impressive performance but their abilities remain highly dependent on prompts which are hand written with onerous trial-and-error effort. We propose a simple and nonparametric solution to this problem, Automatic Prompt Optimization (APO), which is inspired by numerical gradient descent to automatically improve prompts, assuming access to training data and an LLM API. Our experiments suggest this method can outperform prior prompt editing techniques and improve an initial prompt’s performance by up to 31%, by using data to rewrite vague task descriptions into more precise annotation instructions.

Reid Pryzant is a Senior Research Scientist at Microsoft, and former Computer Science PhD at Stanford University advised by Dan Jurafsky. His work has won outstanding research awards from CVPR, AAAI, and the National Science Foundation.

Build Natural Language Applications with txtai
This talk will introduce txtai and show how it can be used for semantic search, LLM orchestration and language model workflows. An overview of the embeddings database architecture will be discussed along with how vector indexes (sparse and dense), graph networks and relational databases connect together. Example use cases will cover SQL-driven vector search, topic modeling and retrieval augmented generation.

David Mezzetti is the founder of NeuML, the company behind txtai. He is building a suite of open-source, easy-to-use, semantic search and workflow applications. Dave previously co-founded and built Data Works into a 50+ person well-respected software services company leading to a successful acquisition.

Zoom: https://voxel51.com/computer-vision-events/fiftyone-workshop-oct-25/

Want greater visibility into the quality of your computer vision datasets and models? Then join Dan Gural, machine learning engineer at Voxel51, for this free 90 minute, hands-on workshop to learn how to leverage the open source FiftyOne computer vision toolset.

In the first part of the workshop we’ll cover:

• FiftyOne Basics (terms, architecture, installation, and general usage)
• An overview of useful workflows to explore, understand, and curate your data
• How FiftyOne represents and semantically slices unstructured computer vision data

The second half will be a hands-on introduction to FiftyOne, where you will learn how to:

• Load datasets from the FiftyOne Dataset Zoo
• Navigate the FiftyOne App
• Programmatically inspect attributes of a dataset
• Add new sample and custom attributes to a dataset
• Generate and evaluate model predictions
• Save insightful views into the data

Prerequisites are a working knowledge of python and basic computer vision. All attendees will get access to the tutorials, videos, and code examples used in the workshop.