Priyank shah
成为会员时间：2025
钻石联赛
12259 积分
Google DeepMind: 07 Accelerate Your Model
Earned Mar 30, 2026 EDT
Google DeepMind: 05 Fine-Tune Your Model
Earned Mar 28, 2026 EDT
Google DeepMind: 04 Discover The Transformer Architecture
Earned Mar 28, 2026 EDT
Google DeepMind: 03 Design And Train Neural Networks
Earned Mar 22, 2026 EDT
Google DeepMind: 02 Represent Your Language Data
Earned Mar 21, 2026 EDT
Google DeepMind: 01 Build Your Own Small Language Model
Earned Mar 19, 2026 EDT
AI 基础设施：网络组建和管理技术
Earned Jan 21, 2026 EST
AI 基础设施：存储选项
Earned Jan 20, 2026 EST
AI 基础设施：部署类型
Earned Jan 18, 2026 EST
AI Infrastructure：Cloud TPU
Earned Jan 17, 2026 EST
AI Infrastructure：Cloud GPU
Earned Jan 11, 2026 EST
AI Infrastructure：AI Hypercomputer 简介
Earned Jan 1, 2026 EST
适用于端到端 SDLC 的 Gemini
Earned Dec 30, 2025 EST
在 Agent Platform 中设计提示
Earned Dec 20, 2025 EST
Responsible AI: 和 Google Cloud 一起践行 AI 原则
Earned Dec 13, 2025 EST
负责任的 AI 简介
Earned Dec 13, 2025 EST
大型语言模型简介
Earned Dec 13, 2025 EST
生成式 AI 简介
Earned Dec 12, 2025 EST
Train more powerful models with a single GPU. In this course, you will learn how hardware can speed up model training and the key considerations when training models on a GPU. First, you will learn how to estimate the number of computations and the amount of computer memory required to train large neural networks. You will then discover techniques for reducing the computing and memory requirements when training a model. Techniques which you will apply for fine-tuning a Gemma model with 4 billion parameters. Finally, you will consider the potential environmental impacts of machine learning, with a focus on where questions of energy, water, and e-waste intersect with justice and equity.
了解详情
Unleash the power of language models with fine-tuning. In this course, you will learn how to adjust a pre-trained model to a specific task. You will start with full-parameter fine-tuning using a small language model. To tune larger models like Gemma, you will learn parameter-efficient techniques with a focus on LoRA. Finally, you will be briefly introduced to reinforcement learning as an alternative to supervised fine-tuning (SFT). You will also explore how AI is imagined and made sense of in cultural contexts. You will consider why responsible AI is not just about technical safety but also about building governance systems that reflect community values and protect the public interest.
了解详情
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.
了解详情
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.
了解详情
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.
了解详情
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.
了解详情
欢迎学习“AI 基础设施：网络组建和管理技术”课程。
在本课程中，您将学习如何利用 Google Cloud 的高带宽、低延迟基础设施，优化 AI 系统所有组件之间的数据传输和通信。学完本课程后，您将了解网络组建和管理在整个 AI 流水线（从数据注入、训练到推理）中发挥的关键作用，并能够应用最佳实践来确保您的工作负载以最高速度运行。
了解详情
在本课程中，您将全面了解 Google Cloud 提供的存储解决方案，这些解决方案专为 AI 和高性能计算 (HPC) 工作负载量身定制。您将学习如何为机器学习生命周期的各个阶段选择合适的存储选项。您将探索如何在训练期间优化 I/O 性能，管理海量数据集以进行数据准备，以及以低延迟部署模型制品。通过实际案例和演示，您将获得设计可靠存储解决方案所需的专业知识，从而加速 AI 创新。
了解详情
本课程全面指导如何在 Google Cloud 上部署、管理和优化 AI 和高性能计算 (HPC) 工作负载。通过一系列课程和实际演示，您将探索从基于 Google Compute Engine (GCE) 的高度可定制环境，到 Google Kubernetes Engine (GKE) 等托管式解决方案的各种部署策略。具体来说，您将学习如何创建集群并部署 GKE 进行推理。
了解详情
欢迎学习 Cloud TPU 课程。我们将探讨 TPU 在不同场景下的优势和劣势，并比较不同的 TPU 加速器，以帮助您选择合适的加速器。您将了解可通过哪些策略充分提高 AI 模型的性能和效率，并理解 GPU/TPU 互操作性对于创建灵活的机器学习工作流程的重要性。通过引人入胜的课程内容和实际演示，您将逐步了解如何有效利用 TPU。
了解详情
对 AI 背后的强大硬件感到好奇吗？本单元将详细讲解性能经过优化的 AI 计算机，向您展示它们为何如此重要。我们将探讨 CPU、GPU 和 TPU 如何让 AI 任务高速运行，介绍它们各自的特点，并说明 AI 软件是如何充分发挥这些硬件的性能的。学习结束后，您将清楚地知道如何为自己的 AI 项目选择合适的 GPU，从而为 AI 工作负载做出明智的决策。
了解详情
准备好探索 AI Hypercomputer 了吗？这门课程将带您轻松入门！我们将介绍相关基础知识，并阐释它们如何助力 AI 处理 AI 工作负载。您将了解超级计算机内部的各个组件，如 GPU、TPU 和 CPU，并知晓如何根据您的需求选择合适的部署方法。
了解详情
在本课程中，您将了解 Gemini（Google Cloud 推出的一款依托生成式 AI 的协作工具）如何帮助您使用 Google 产品和服务开发、测试、部署和管理应用。在 Gemini 的协助下，您可以学习如何开发和构建 Web 应用、修复应用中的错误、开发测试和查询数据。您可以通过实操实验了解如何利用 Gemini 来改进软件开发生命周期 (SDLC)。
Duet AI 已更名为 Gemini，这是我们的新一代模型。
了解详情
完成 在 Agent Platform 中设计提示入门技能徽章课程，展示以下方面的技能： Agent Platform 中的提示工程、图片分析和多模态生成式技术。探索如何编写有效的提示，指导生成式 AI 输出， 以及将 Gemini 模型应用于真实的营销场景。
了解详情
随着企业对人工智能和机器学习的应用越来越广泛，以负责任的方式构建这些技术也变得更加重要。但对很多企业而言，真正践行 Responsible AI 并非易事。如果您有意了解如何在组织内践行 Responsible AI，本课程正适合您。
本课程将介绍 Google Cloud 目前如何践行 Responsible AI，以及从中总结的最佳实践和经验教训，便于您以此为框架构建自己的 Responsible AI 方法。
了解详情
这是一节入门级微课程，旨在解释什么是负责任的 AI、它的重要性，以及 Google 如何在自己的产品中实现负责任的 AI。此外，本课程还介绍了 Google 的 7 个 AI 开发原则。
了解详情
这是一节入门级微学习课程，探讨什么是大型语言模型 (LLM)、适合的应用场景以及如何使用提示调整来提升 LLM 性能，还介绍了可以帮助您开发自己的 Gen AI 应用的各种 Google 工具。
了解详情
这是一节入门级微课程，旨在解释什么是生成式 AI、它的用途以及与传统机器学习方法的区别。该课程还介绍了可以帮助您开发自己的生成式 AI 应用的各种 Google 工具。
了解详情
