Adrian Ramlal
Учасник із 2019
Бронзова ліга
Кількість балів: 5370
Учасник із 2019
The Generative AI Explorer - Vertex Quest is a collection of labs on how to use Generative AI on Google Cloud. Through the labs, you will learn about how to use the models in the Vertex AI PaLM API family, including text-bison, chat-bison, and textembedding-gecko. You will also learn about prompt design, best practices, and how it can be used for ideation, text classification, text extraction, text summarization, and more. You will also learn how to tune a foundation model by training it via Vertex AI custom training and deploy it to a Vertex AI endpoint.
This course introduces Vertex AI Studio, a tool to interact with generative AI models, prototype business ideas, and launch them into production. Through an immersive use case, engaging lessons, and a hands-on lab, you’ll explore the prompt-to-product lifecycle and learn how to leverage Vertex AI Studio for Gemini multimodal applications, prompt design, prompt engineering, and model tuning. The aim is to enable you to unlock the potential of gen AI in your projects with Vertex AI Studio.
This course teaches you how to create an image captioning model by using deep learning. You learn about the different components of an image captioning model, such as the encoder and decoder, and how to train and evaluate your model. By the end of this course, you will be able to create your own image captioning models and use them to generate captions for images
This course introduces you to the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model. You learn about the main components of the Transformer architecture, such as the self-attention mechanism, and how it is used to build the BERT model. You also learn about the different tasks that BERT can be used for, such as text classification, question answering, and natural language inference.This course is estimated to take approximately 45 minutes to complete.
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.
This course will introduce you to the attention mechanism, a powerful technique that allows neural networks to focus on specific parts of an input sequence. You will learn how attention works, and how it can be used to improve the performance of a variety of machine learning tasks, including machine translation, text summarization, and question answering. This course is estimated to take approximately 45 minutes to complete.
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.
Зараз усі говорять про масиви даних, машинне навчання й штучний інтелект, але це досить вузькоспеціалізовані теми, про які важко знайти матеріали, зрозумілі не лише спеціалістам. На щастя, Google Cloud пропонує зручні сервіси в цих галузях, а завдяки цьому вступному курсу ви зможете ознайомитися з такими інструментами, як BigQuery, Cloud Speech API і Video Intelligence.
Workspace is Google's collaborative applications platform, delivered from Google Cloud. In this introductory-level course you will get hands-on practice with Workspace’s core applications from a user perspective. Although there are many more applications and tool components to Workspace than are covered here, you will get experience with the primary apps: Gmail, Calendar, Sheets and a handful of others. Each lab can be completed in 10-15 minutes, but extra time is provided to allow self-directed free exploration of the applications.
Якщо ви лише пробуєте розробляти хмарні рішення й шукаєте практичні заняття на додаток до кваліфікаційного курсу "Знайомство з Google Cloud", тоді цей курс саме для вас. Ви отримаєте прикладний досвід завдяки практичним заняттям, присвяченим Cloud Storage і іншим ключовим сервісам додатків, як-от Monitoring і Cloud Functions. Ви отримаєте цінні навички, які можна застосовувати в будь-яких проєктах Google Cloud.
Пройдіть квест Create and Manage Cloud Resources й отримайте skill badge. Ви навчитеся виконувати наведені нижче дії. Писати команди gcloud і використовувати Cloud Shell, створювати й розгортати віртуальні машини в Compute Engine, запускати контейнерні додатки за допомогою Google Kubernetes Engine, а також налаштовувати розподілювачі навантаження для мережі й HTTP.Skill badge – це ексклюзивна цифрова винагорода, яка підтверджує, що ви вмієте працювати з продуктами й сервісами Google Cloud, а також застосовувати ці знання в інтерактивному практичному середовищі. Щоб отримати skill badge й показати його колегам, пройдіть цей квест і підсумковий тест.