Miembro desde 2025
Google DeepMind: 02 Represent Your Language Data
Earned dic 30, 2025 EST
Introducción a los modelos de lenguaje grandes
Earned dic 19, 2025 EST
Innova con la Inteligencia Artificial de Google Cloud
Earned dic 19, 2025 EST
Google DeepMind: 01 Build Your Own Small Language Model
Earned dic 18, 2025 EST
In this Google DeepMind course you will learn how to prepare text data for language models to process. You will investigate the tools and techniques used to prepare, structure, and represent text data for language models, with a focus on tokenization and embeddings. You will be encouraged to think critically about the decisions behind data preparation, and what biases within the data may be introduced into models. You will analyze trade-offs, learn how to work with vectors and matrices, how meaning is represented in language models. Finally, you will practice designing a dataset ethically using the Data Cards process, ensuring transparency, accountability, and respect for community values in AI development.
Este es un curso introductorio de microaprendizaje en el que se explora qué son los modelos de lenguaje grandes (LLM), sus casos de uso y cómo se puede utilizar el ajuste de instrucciones para mejorar el rendimiento de los LLM. También abarca las herramientas de Google para ayudarte a desarrollar tus propias aplicaciones de IA generativa.
La inteligencia artificial (IA) y el aprendizaje automático (AA) representan una evolución importante en las tecnologías de la información que están transformando rápidamente una amplia variedad de sectores. En el curso “Innova con la Inteligencia Artificial de Google Cloud”, se exploran las maneras en que las organizaciones pueden usar la IA y el AA para transformar sus procesos empresariales. Como parte de la ruta de aprendizaje de Líder digital de Cloud, el objetivo de este curso es ayudar a las personas a crecer en su rol y desarrollar el futuro de su empresa.
In this Google DeepMind course, you will learn the fundamentals of language models and gain a high-level understanding of the machine learning development pipeline. You will consider the strengths and limitations of traditional n-gram models and advanced transformer models. Practical coding labs will enable you to develop insights into how machine learning models work and how they can be used to generate text and identify patterns in language. Through real-world case studies, you will build an understanding around how research engineers operate. Drawing on these insights you will identify problems that you wish to tackle in your own community and consider how to leverage the power of machine learning responsibly to address these problems within a global and local context.