Member since 2025
Google DeepMind: 03 Design And Train Neural Networks
Earned Mar 26, 2026 EDT
Google DeepMind: 02 Represent Your Language Data
Earned Jan 23, 2026 EST
Google DeepMind: 01 Build Your Own Small Language Model
Earned Jan 4, 2026 EST
Google DeepMind：訓練小型語言模型
Earned Dec 30, 2025 EST
在 Google Cloud 實作 Sensitive Data Protection
Earned Dec 24, 2025 EST
Google Cloud Compute 基本操作
Earned Oct 18, 2025 EDT
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.
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.
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.
完成「Google DeepMind：訓練小型語言模型」技能徽章進階課程，即可證明您具備下列技能： 提出實際的語言模型研究問題、建構簡單的分詞器、準備資料集來訓練 Transformer 語言模型，以及執行小型語言模型的訓練迴圈。 註冊免付費訂閱方案，即可免付費使用這個實驗室。每個月還能獲得 35 點免付費抵免額！
完成 在 Google Cloud 實作 Sensitive Data Protection 技能徽章入門課程，證明您具備下列技能：使用 Sensitive Data Protection 服務 (包括 Cloud Data Loss Prevention API) 來檢查、遮蓋及去識別化 Google Cloud 中的機密資料。
完成「Google Cloud Compute 基本操作」任務， 學習如何在 Compute Engine 中使用虛擬機器 (VM)、永久磁碟 和網路伺服器，即可獲得技能徽章。