Keras

A talk featuring some introductory materials on Generative AI and why it matters to students and early-career professionals, followed by some details about the Vertex AI Model Garden and how they can leverage it for quickly developing and validating ideas related to Image and Text Generation.

A comprehensive talk that comprised of 3 separate sessions, each detailing different levels of understanding of Machine Learning with focus on Beginner, Intermediate, and Advanced topics.

A talk detailing how to get started with Distributed Training, important concepts and historical context, potential issues and troubleshooting tips, along with a demo on how they can use it with Keras and JAX

A talk around Computer Vision, the most important concepts to know from the field at the intersection of Deep Learning and Computer Vision, and how to use KerasCV to accelerate development.

A talk around Computer Vision, the most important concepts to know from the field at the intersection of Deep Learning and Computer Vision, and how to use KerasCV to accelerate development.

A talk that explores the Vertex AI suite of features that can help jumpstart a developer’s journey into AI-powered tooling, and how Model Garden can be used to make interesting features through APIs.

A talk around Computer Vision, the most important concepts to know from the field at the intersection of Deep Learning and Computer Vision, and how to use KerasCV to accelerate development.

A talk around Computer Vision, the most important concepts to know from the field at the intersection of Deep Learning and Computer Vision, and how to use KerasCV to accelerate development.

A talk around Computer Vision, the most important concepts to know from the field at the intersection of Deep Learning and Computer Vision, and how to use KerasCV to accelerate development.

A talk that introduced a first-in-class integration of Keras with Pytorch-based HF models within Transformers through a hands-on workshop for approximately 40 folks in a joint talk with Soumik Rakshit (ML GDE, MLE @ Weights & Biases)