Designing with Adobe Firefly and Photoshop - Adobe Live
Explore Firefly and Photoshop Beta's Generative Fill feature with expert designers, gaining insights into their creative process and innovative design techniques.
Introduction to Deep Learning
Deep Learning is subset of machine learning focused on extracting patterns from data using neural networks and use those patterns to inform the learning tasks.
How to Use Generative AI - GPT-3 and AI Voices
Explore Generative AI, including GPT-3 and AI Voices, to create engaging content. Learn about types, examples, and applications through demonstrations and live demos.
DreamBooth: Personalized Text-to-Image Generation - Lecture 10
Explore DreamBooth's innovative approach to AI-generated imagery, including unique identifiers, recontextualization, and artistic renditions. Gain insights into its capabilities and limitations.
Open Source Generative AI in Question-Answering - NLP Using Python
Learn to build a generative question-answering system using open-source AI and Python. Covers architecture, data preprocessing, embedding, indexing, and querying with BART for multi-sentence answers to open-ended questions.
Diffusion Models - PyTorch Implementation
Learn to implement and train diffusion models in PyTorch, covering unconditional and conditional versions, classifier-free guidance, and exponential moving average. Gain hands-on experience with state-of-the-art generative AI techniques.
ChatGPT - Impact of AI on Technology - Introduction to Generative AI and Prompt Engineering
Explore ChatGPT's impact, from its core to applications. Uncover Generative AI, GPT models, and prompt engineering. Discover how AI is reshaping digital communication and pushing technological boundaries.
Lessons Learned from Using Generative AI as a Role-playing Tool in Data Science Education - April 2024
Exploring Generative AI for personalized learning in Data Science education, focusing on interactive Q&A sessions and AI-generated quizzes to enhance understanding of concepts like Linear Regression.
Stable Diffusion
Explore latent diffusion models for AI image generation, including unconditional generation and super-resolution techniques.
How ChatGPT and Generative AI Will Shape the Future of Work
Explore the impact of Generative AI on workplaces with Stanford professor Arvind Karunakaran. Learn about emerging capabilities, challenges, and how to prepare your organization for this transformative technology.
Denoising Diffusion Models for Denoising Diffusion MRI - Tiange Xiang
Explore innovative self-supervised denoising techniques for diffusion MRI using denoising diffusion models. Learn about a three-stage framework integrating statistic-based theory for improved medical imaging quality.
Forecasting Disruption: Assessing Technological Risks and Opportunities
Explore forecasting techniques to assess technological disruptions' impact on business models, with insights on evaluating risks and opportunities to inform strategic decisions.
From Flat to Phantasmal: Enhancing User Experiences Through Spatial Computing Advancements
Exploring spatial computing advancements to enhance user experiences, focusing on streamlined creation tools, personalized accessibility, and integration of adjacent technologies for immersive mixed-reality applications.
Deep Generative Models - Autoregressive Models - Lecture 3
Explore autoregressive models in deep learning, focusing on their architecture, training, and applications in generating sequential data like text and images.
Stanford CS236: Deep Generative Models - Score Based Diffusion Models - Lecture 16
Explore score-based diffusion models in deep generative modeling, covering theoretical foundations and practical applications in AI and machine learning.
Deep Generative Models - Diffusion Models for Discrete Data - Lecture 18
Explore diffusion models for discrete data in this Stanford lecture, covering advanced techniques in deep generative modeling for AI applications.
Deep Generative Models - Lecture 6: Variational Autoencoders
Explore Variational Autoencoders (VAEs) in deep generative modeling, covering theoretical foundations and practical applications in this Stanford lecture by Prof. Stefano Ermon.
Deep Generative Models - Energy Based Models - Lecture 11
Explore energy-based models in deep learning, their applications, and theoretical foundations. Gain insights from Stanford's expert on advanced generative modeling techniques.
Stanford CS236 - Deep Generative Models - Lecture 1: Introduction
Explore deep generative models with Stanford's Prof. Ermon. Learn key concepts, applications, and cutting-edge techniques in this comprehensive introduction to a rapidly evolving field of AI.
From Large Language Models to Large Multimodal Models - Stanford CS25 - Lecture 4
Explore the evolution from large language models to multimodal AI, focusing on CogVLM and CogAgent. Learn about integrating visual perception with language capabilities and future research directions.
AI in the Workplace: Rethinking Skill Development
Explore the impact of Generative AI on workplace training, employee duties, and skill development. Learn strategies for integrating AI tools to enhance individual and team performance in the evolving work landscape.