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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/custom-distributed-training-with-tensorflow
课程评论：没有评论
课程名称：TensorFlow的自定义和分布式训练 课程概述： 在本课程中，您将学习： - 了解TensorFlow的基本构建块——张量对象，理解张量流中急切模式和图模式的区别，并学习如何使用TensorFlow工具计算梯度。 - 使用GradientTape和TensorFlow数据集构建自己的自定义训练循环，以获得更多灵活性和对模型训练的可视化。 - 学习生成图模式代码的好处，了解图代码的样子，并练习使用TensorFlow的工具自动生成更高效的代码。 - 利用分布式训练的优势，更快地处理更多数据和训练更大的模型，概述各种分布式训练策略，实践在多个GPU核心和多个TPU核心上训练的策略。 该课程是DeepLearning.AI TensorFlow：高级技术专业化的一部分，旨在介绍TensorFlow中提供给学习者更多控制模型架构的功能，以及帮助他们创建和训练高级机器学习模型的工具。 适合对象： 此专业化课程适合具有基础TensorFlow知识的早期和中期职业软件及机器学习工程师，旨在通过学习高级TensorFlow功能来扩展他们的知识和技能，以构建强大的模型。 课程大纲： 1. **微分和梯度** - 本周将详细了解TensorFlow的基本构建块——张量对象。您将能够描述急切模式和图模式的区别，并解释为何急切模式对开发者非常友好。同时，您将使用TensorFlow工具计算梯度，省去查找旧高等数学教材的时间。 2. **自定义训练** - 本周您将使用GradientTape和TensorFlow数据集构建自定义训练循环。撰写自己的训练循环将给予您更多的灵活性和对模型训练的可视化。您还将使用函数计算函数的导数，避免查阅旧高等数学教材。 3. **图模式** - 本周您将学习生成图模式代码的好处，了解图代码的样子，并练习使用TensorFlow的工具自动生成这种更高效的代码，无需自己编写图代码。 4. **分布式训练** - 本周，您将利用分布式训练的力量，更快地处理更多数据并训练更大的模型。您将概述多种分布式训练策略，并练习在多个GPU核心和多个TPU核心上训练的两种策略。准备好展现您的超能力吧！
Name:Differentiation and Gradients
Description:This week, you will get a detailed look at the fundamental building blocks of TensorFlow - tensor objects. For example, you will be able to describe the difference between eager mode and graph mode in TensorFlow, and explain why eager mode is very user friendly for you as a developer. You will also use TensorFlow tools to calculate gradients so that you don’t have to look for your old calculus textbooks next time you need to get a gradient!
Name:Custom Training
Description:This week, you will build custom training loops using GradientTape and TensorFlow Datasets. Being able to write your own training loops will give you more flexibility and visibility with your model training. You will also use a function to calculate the derivatives of functions so that you don’t have to look to your old calculus textbooks to calculate gradients.
Name:Graph Mode
Description:This week, you’ll learn about the benefits of generating code that runs in “graph mode”. You’ll take a peek at what graph code looks like, and you’ll practice generating this more efficient code automatically with TensorFlow’s tools, so that you don’t have to write the graph code yourself!
Name:Distributed Training
Description:This week, you will harness the power of distributed training to process more data and train larger models, faster. You’ll get an overview of various distributed training strategies and then practice working with two strategies, one that trains on multiple GPU cores, and the other that trains on multiple TPU cores. Get your cape ready, because you’re going to get some superpowers this week!
In this course, you will: • Learn about Tensor objects, the fundamental building blocks of TensorFlow, understand the difference between the eager and graph modes in TensorFlow, and learn how to use a TensorFlow tool to calculate gradients. • Build your own custom training loops using GradientTape and TensorFlow Datasets to gain more flexibility and visibility with your model training. • Learn about the benefits of generating code that runs in graph mode, take a peek at what graph code looks like, and practice generating this more efficient code automatically with TensorFlow’s tools. • Harness the power of distributed training to process more data and train larger models, faster, get an overview of various distributed training strategies, and practice working with a strategy that trains on multiple GPU cores, and another that trains on multiple TPU cores. The DeepLearning.AI TensorFlow: Advanced Techniques Specialization introduces the features of TensorFlow that provide learners with more control over their model architecture and tools that help them create and train advanced ML models. This Specialization is for early and mid-career software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models.
