Build your ultimate AI agent
课程介绍
可扩展 AI 基础
这门课程将带你踏上可扩展 AI 基础知识之旅。 你将首先学习 PyTorch Lightning 如何通过减少样板代码来简化模型开发生命周期。 通过引导式示例，你将了解如何将复杂的神经网络拆分为可复用的组件，从而即使项目规模不断扩大，也能保持代码质量。高级优化技术
你还将掌握优化技术，例如自适应优化器、模型剪枝和量化。 您将亲眼看到训练策略中的微小变化如何带来速度和准确率的显著提升，并学习如何优化训练循环以消除瓶颈。生产就绪部署
到课程结束时，您将掌握将原型一路推进到生产环境的技能，并拥有一套模块化、经过优化且可部署的 AI 解决方案作品集，随时准备应对现实世界的挑战。先修要求
课程大纲
课程大纲
1
Building Scalable Models with PyTorch Lightning
In this chapter, we'll explore how PyTorch Lightning simplifies the development and deployment of scalable AI models. Starting with foundational concepts, we'll go through the core structure of a PyTorch Lightning project, including essential components like the LightningModule and Trainer, to set a strong foundation for more advanced AI solutions.
- Introduction to PyTorch Lightning50 经验值
- Introducing the LightningModule100 经验值
- Running the Lightning Trainer100 经验值
- Defining models with LightningModule50 经验值
- Usage of the LightningModule50 经验值
- Mastering the init method100 经验值
- Perfecting the forward method100 经验值
- Implementing training logic50 经验值
- Implementing the training step100 经验值
- Configuring the optimizer100 经验值
- Training and evaluating100 经验值
2
Advanced Techniques in PyTorch Lightning
We'll dive deeper into PyTorch Lightning to efficiently manage data and refine model training in this chapter. We'll learn how to create modular and reusable data workflows with LightningDataModule, evaluate your models accurately through validation and testing, and enhance training processes using Lightning Callbacks to automate model improvement and avoid overfitting.
3
Optimizing Models for Scalability
Learn to prepare deep learning models for real-world deployment by making them leaner and faster. This chapter introduces techniques such as dynamic quantization, pruning, and TorchScript conversion, helping you reduce model size and latency without sacrificing accuracy
Scalable AI Models with PyTorch Lightning
课程完成
