Google generative AI courses

[XuperX]

Google has created a Generative AI learning path with 9 FREE courses! Topics cover:

• Intro to LLMs
• Attention Mechanism
• Image Generation/Captioning
• Intro to Responsible AI From the fundamentals of LLMs to creating & deploying generative AI solutions! :rocket: Read more :thread::point_down: :one: Introduction to Generative AI: An introductory level micro-learning course aimed at explaining:
• What Generative AI is
• How it is used
• How it differs from traditional ML Check this out:point_down: https://lnkd.in/duKJ3sm6 :two: Introduction to Large Language Models: The course explores:
• Fundamentals LLMs
• Their use cases
• Prompt engineering on LLMs Check this out:point_down: https://lnkd.in/dm_yS4MQ :three: Introduction to Responsible AI: The course explains what responsible AI is, why it's important, and how Google implements responsible AI in their products. Check this out:point_down: https://lnkd.in/dV8zNvwm :four: Introduction to Image Generation: This course introduces diffusion models, a family of ML models that recently showed promise in the image generation space. Check this out:point_down: https://lnkd.in/dqcZBZqd :five: Encoder-Decoder Architecture: This course gives you a synopsis of the encoder-decoder architecture. It's a powerful and prevalent machine learning architecture for sequence-to-sequence tasks. Check this out:point_down: https://lnkd.in/dhDhUgwJ :six: Attention Mechanism: The course teaches you how attention works & how it revolutionised:
• machine translation
• text summarisation
• question answering Check this out:point_down: https://lnkd.in/dwsZZw6j :seven: Transformer Models and BERT Model: This course introduces you to some of the most famous and effective transformer architectures! Check this out:point_down: https://lnkd.in/dK4p3n2s :eight: Create Image Captioning Models: This course teaches you how to create an image captioning model by using deep learning. Check this out:point_down: https://lnkd.in/d8w32x5Y :nine: Introduction to Generative AI Studio: This course introduces Generative AI Studio, a product on Vertex AI. It teaches you to prototype and customize generative AI models so you can use their capabilities in your applications. QwiklabsQwiklabs Introduction to Generative AI | Google Cloud Skills Boost This is an introductory level microlearning course aimed at explaining what Generative AI is, how it is used, and how it differs from traditional machine learning methods. It also covers Google Tools to help you develop your own Gen AI apps.This course is estimated to take approximately 45 minutes to complete. QwiklabsQwiklabs Introduction to Large Language Models | Google Cloud Skills Boost This is an introductory level microlearning course that explores what large language models (LLM) are, the use cases where they can be utilized, and how you can use prompt tuning to enhance LLM performance. It also covers Google tools to help you develop your own Gen AI apps. This course is estimated to take approximately 45 minutes to complete. QwiklabsQwiklabs Introduction to Responsible AI | Google Cloud Skills Boost This is an introductory-level microlearning course aimed at explaining what responsible AI is, why it's important, and how Google implements responsible AI in their products. It also introduces Google's 7 AI principles. QwiklabsQwiklabs Introduction to Image Generation | Google Cloud Skills Boost This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Diffusion models draw inspiration from physics, specifically thermodynamics. Within the last few years, diffusion models became popular in both research and industry. Diffusion models underpin many state-of-the-art image generation models and tools on Google Cloud. This course introduces you to the theory behind diffusion models and how to train and deploy them on Vertex AI. QwiklabsQwiklabs Encoder-Decoder Architecture | Google Cloud Skills Boost This course gives you a synopsis of the encoder-decoder architecture, which is a powerful and prevalent machine learning architecture for sequence-to-sequence tasks such as machine translation, text summarization, and question answering. You learn about the main components of the encoder-decoder architecture and how to train and serve these models. In the corresponding lab walkthrough, you’ll code in TensorFlow a simple implementation of the encoder-decoder architecture for poetry generation from the beginning.