Participante desde 2026
Google DeepMind: 02 Represent Your Language Data
Earned Aug 9, 2026 EDT
Google DeepMind: treine um modelo de linguagem pequeno
Earned Aug 9, 2026 EDT
Google DeepMind: 01 Build Your Own Small Language Model
Earned Aug 9, 2026 EDT
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.
Receba o selo de habilidade avançado Google DeepMind: treinar um modelo de linguagem pequeno ao concluir este curso para demonstrar habilidades nas seguintes atividades:formular problemas de pesquisa de modelos de linguagem do mundo real, criar um tokenizador simples, preparar um conjunto de dados para treinar um modelo de linguagem de transformador e executar o loop de treinamento de um modelo de linguagem pequeno. Acesse este laboratório sem custo financeiro ao se inscrever na assinatura sem custo financeiro. Receba 35 créditos sem custo financeiro por mês!
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.