07-11, 08:00–12:00 (America/Chicago), Classroom 202
This tutorial introduces Keras, a powerful deep learning library and demonstrates how to enable generative models using Keras. The first part delves into the Keras training pipeline and extended modules. The second part explores image generative models using stable diffusion, with live coding examples to generate novel images and teach the model new concepts. Finally, you'll explore language generative models, including GPT and BART, with a live coding example that demonstrates how to enable these models. By the end of this tutorial, you'll have a solid understanding of how to harness Keras to create powerful AI applications.
In this tutorial, we will explore the powerful Keras library and the world of generative models in AI. We will begin with a brief introduction to Keras, its history, and its value in creating neural networks. We will then dive into the Keras training pipeline, exploring sequential, functional, and custom models, optimizers, loss and metrics, and the training API. We will also cover Keras extended modules for NLP, CV, and GNN, and walk through an end-to-end example to create and optimize a model.
In the second part of the tutorial, we will specifically focus on image generative model stable diffusion architecture. We will explain stable diffusion, demonstrate a latent space walkthrough, and generate images using a colab example. Additionally, we will focus on image inpainting and teaching stable diffusion new concepts, this is called textual inversion.
Finally, we will explore how generative models work in NLP, specifically focusing on GPT structure and GPT 2, BART, and the mobile playbook. We will demonstrate XLA compilation and show how general support for text generation using one API can be achieved. By the end of this tutorial, attendees will have a solid understanding of Keras and generative models and how they can be used to create powerful AI applications.
Python
Basic understanding of machine learning and deep learning
Register with Google colab. If you have a google/gmail account, it's just one-click registration.
Divya is a talented machine learning software engineer who is currently a part of the Keras team at Google. In this role, she specializes in developing Keras core modeling APIs and KerasCV to improve the functionality of the software.
Divya has an impressive track record of delivering successful conference talks, including the Southern Data Science Conference and the Women in ML Symposium. Prior to joining Google, Divya worked as a Deep Learning Scientist for Zazu Sensor, a startup group in Intel's Emerging Growth Incubation (EGI) group. Her work there focused on computer vision and deep learning algorithm development for object detection and tracking, resulting in significant advancements for the startup.
Before her time at Zazu Sensor, Divya worked as a Platform Architect at Intel's Client Computing Group, where she was responsible for developing proof of concepts for innovative solutions in anonymized computer vision applications. Her efforts resulted in several successful patents being filed, bringing substantial value to the organization.
Divya completed her Masters in Computer Engineering from Texas A & M University where she focused on Artificial intelligence in 2017.
Chen Qian is a software engineer at Google. He is a maintainer of Keras and Tensorflow. In 2021, Chen co-founded the project KerasNLP with other Keras maintainers, and has since been working on building APIs for NLP developers. He is enthusiastic at languages, finding everything about language is charming, e.g., learning new languages, linguistics and NLP.