In this Google DeepMind course you will discover the mechanisms of the transformer architecture. You will investigate how transformer language models process prompts to make context-sensitive next-token predictions. Through practical activities you will explore the attention mechanism, visualize attention weights, and encounter advanced concepts like masked attention and multi-head attention. You will also learn other techniques that are necessary to build neural networks that are well-suited to be used as language models. Finally, through activities on values, stakeholder mapping and community engagement, you will practice concrete tools for ensuring AI projects are developed with communities, not just for them.
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In this Google DeepMind course you will focus on the training process for machine learning models. You will learn how to spot and mitigate issues when training a model, such as overfitting and underfitting. In practical coding labs, you will implement and evaluate the multilayer perceptron for simple classification tasks. This will provide insights into the mechanics of training a neural network model and the backpropagation algorithm. Research case studies will demonstrate how neural networks power real-world models. Additionally, you will consider the broader social impacts of innovation by looking beyond immediate benefits to anticipate potential risks, safety concerns, and further-reaching societal consequences.
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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.
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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.
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Bu kursta Vertex AI Studio tanıtılmaktadır. Bu araç, üretken yapay zeka modelleriyle etkileşime geçmek, kurumsal fikirlerin prototipini oluşturmak ve bunları gerçek hayatta uygulamak için kullanılır. Gerçek hayattan kullanım alanları, etkileşimli dersler ve uygulamalı laboratuvarlar aracılığıyla, ilk istemden son ürüne uzanan yaşam döngüsünü keşfedecek ve çoklu format destekli Gemini uygulamaları, istem tasarımı, istem mühendisliği ve model ayarlama konularında Vertex AI Studio'dan nasıl yararlanabileceğinizi öğreneceksiniz. Bu kursun amacı, Vertex AI Studio'yu kullanarak projelerinizde üretken yapay zekadan yararlanabilmenizi sağlamaktır.
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The Generative AI Explorer - Agent Platform course 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 Gemini models in Agent Platform. 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 custom training in Agent Platform and deploy it to an endpoint in Agent Platform.
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Kurumsal yapay zeka ve makine öğreniminin kullanımı arttıkça, bu teknolojileri sorumlu bir şekilde geliştirmenin önemi de artıyor. Birçok kişi için sorumlu yapay zeka hakkında konuşmak, bunu uygulamaya geçirmekten daha kolaydır. Kuruluşunuzda sorumlu yapay zekayı nasıl hayata geçireceğinizi öğrenmek istiyorsanız bu kurs tam size göre.
Bu kursta, kendi sorumlu yapay zeka yaklaşımınızı geliştirirken size rehberlik edecek bir çerçeve oluşturmak adına Google Cloud'un bu süreci günümüzde nasıl yürüttüğünü, en iyi uygulama örneklerini ve edindiğimiz tecrübeleri keşfedeceksiniz.
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Earn the intermediate skill badge by completing the Build and Deploy Machine Learning Solutions on Vertex AI skill badge course, where you learn how to use Google Cloud's Vertex AI platform, AutoML, and custom training services to train, evaluate, tune, explain, and deploy machine learning models.
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These labs help you get started with the skills you need to analyze sports-related data. At the end of each lab, you’ll have hands-on experience with one or more of Google Cloud’s powerful data tools. Complete this game to earn a badge, and you’ll be one step closer to completing the challenge. Race the clock to increase your score and watch your name rise on the leaderboard!
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Welcome to the Learn to Earn Cloud Data Challenge! These labs help you get started with data analysis skills. At the end of each lab, you’ll have hands-on experience with one or more of Google Cloud’s powerful data tools. Complete this game to earn a badge, and you’ll be one step closer to completing the challenge. Race the clock to increase your score and watch your name rise on the leaderboard!
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Complete the intermediate Build a Data Warehouse with BigQuery skill badge course to demonstrate skills in the following: joining data to create new tables, troubleshooting joins, appending data with unions, creating date-partitioned tables, and working with JSON, arrays, and structs in BigQuery.
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Google Cloud Fundamentals: Core Infrastructure introduces important concepts and terminology for working with Google Cloud. Through videos and hands-on labs, this course presents and compares many of Google Cloud's computing and storage services, along with important resource and policy management tools.
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Giriş düzeyindeki Compute Engine İçin Cloud Load Balancing'i Uygulama beceri rozetini tamamlayarak şu konulardaki becerilerinizi gösterin: Compute Engine'de sanal makineler oluşturma ve dağıtma. Ağ ve uygulama yük dengeleyicileri yapılandırma.
Google DeepMind: 04 Discover The Transformer Architecture
Earned Eki 25, 2025 EDT
Google DeepMind: 03 Design And Train Neural Networks
Earned Eki 25, 2025 EDT
Google DeepMind: 02 Represent Your Language Data
Earned Eki 24, 2025 EDT
Google DeepMind: 01 Build Your Own Small Language Model
Earned Eki 24, 2025 EDT
Vertex AI Studio'ya Giriş
Earned Eyl 16, 2023 EDT
Generative AI Explorer - Agent Platform
Earned Eyl 16, 2023 EDT
Sorumlu Yapay Zeka: Google Cloud ile Yapay Zeka İlkelerini Uygulama
Earned Mar 4, 2023 EST
DEPRECATED Build and Deploy Machine Learning Solutions on Vertex AI
Earned Tem 5, 2022 EDT
Learn to Earn Cloud Challenge: Sports Data Analysis Skills
Earned Tem 4, 2022 EDT
Learn to Earn Cloud Data Challenge: Data Analyst Skills
Earned Tem 4, 2022 EDT
Build a Data Warehouse with BigQuery
Earned Haz 20, 2022 EDT
Google Cloud Fundamentals: Core Infrastructure
Earned Haz 11, 2022 EDT
Compute Engine İçin Cloud Load Balancing'i Uygulama
Earned Haz 5, 2022 EDT