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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/machine-learning-aws-nvidia
课程评论：没有评论
课程名称：AWS和NVIDIA的动手机器学习 概述：机器学习（ML）项目往往复杂、繁琐且耗时。AWS和NVIDIA通过快速、有效且易于使用的能力解决了这一挑战，让您的ML项目更具效率。本课程专为具备机器学习工作流程基础知识的ML从业人员（包括数据科学家和开发者）设计。在课程中，您将获得在亚马逊SageMaker和基于NVIDIA GPU的亚马逊EC2实例上构建、训练和部署可扩展机器学习模型的实践经验。亚马逊SageMaker为数据科学家和开发者提供了一系列专为机器学习设计的能力，使其能够快速准备、构建、训练和部署高质量的ML模型。此外，基于NVIDIA GPU的亚马逊EC2实例及其软件为高效模型训练和具有成本效益的模型推理托管提供了高性能的GPU优化实例。 课程内容将首先介绍亚马逊SageMaker和NVIDIA GPU的基本概念。随后，学员将通过运行GPU驱动的亚马逊SageMaker笔记本实例进行实践。接着，您将学习如何准备数据集以进行模型训练，构建模型，执行模型训练，以及部署和优化ML模型。此外，学员还将学习如何将这一工作流程应用于计算机视觉（CV）和自然语言处理（NLP）的案例。完成本课程后，您将能够在亚马逊SageMaker中构建、训练、部署和优化GPU加速的ML工作流程，并了解与计算机视觉和NLP相关的亚马逊SageMaker的关键服务。 课程大纲： 第1部分：亚马逊SageMaker和NVIDIA GPU简介 描述：了解亚马逊SageMaker内为现代机器学习（ML）设计的专用工具，使用亚马逊SageMaker Studio IDE准备、构建、训练、调优、部署和管理自己的ML模型，以及深入了解NVIDIA GPU和AWS上的实例。 第2部分：利用RAPIDS和亚马逊SageMaker加速机器学习工作流程 描述：应用对NVIDIA GPU和亚马逊SageMaker的知识，学习设置亚马逊SageMaker的步骤，包括数据获取与转换、模型设计与训练、超参数优化、AutoML及GPU加速推理的评估。 第3部分：计算机视觉 描述：探讨深度学习在计算机视觉（CV）中的应用，学习如何在亚马逊SageMaker上构建一个端到端的对象检测模型，利用NVIDIA GPU处理像素格式的数据。 第4部分：自然语言处理 描述：学习深度学习技术在语言理解中的应用，探讨语言建模、BERT语言模型在搜索、办公软件和语音助手中的应用，以及NVIDIA GPU在训练和部署NLP模型中的优势。 通过本课程，您将能够掌握现代机器学习的关键技能，提升您的职业能力。
Part: 1
Title:Introduction to Amazon SageMaker and NVIDIA GPUs
Description:In this module, you will learn about the purpose-built tools available within Amazon SageMaker for modern machine learning (ML). This includes a tour of the Amazon SageMaker Studio IDE that can be used to prepare, build, train and tune, and deploy and manage your own ML models. Then you will learn how to use Amazon SageMaker classic notebooks and Amazon SageMaker Studio notebooks to develop natural language processing (NLP), computer vision (CV), and other ML models using NVIDIA RAPIDS. You will also dive deep into NVIDIA GPUs, the NVIDIA NGC Catalog, and instances available on AWS for ML.
Part: 2
Title:GPU Accelerated Machine Learning Workflows with RAPIDS and Amazon SageMaker
Description:In this module, you will apply your knowledge of NVIDIA GPUs and Amazon SageMaker. You will learn a background on GPU accelerated machine learning and perform the steps required to setup Amazon SageMaker. You will then learn about data acquisition and data transformation, moving on to model design and training, and finish up by evaluating hyperparameter optimization, AutoML, and GPU accelerated inferencing.
Part: 3
Title:Computer Vision
Description:In this module you will learn about the application of deep learning for Computer Vision (CV). As humans, nature devoted half of our brains to visual processing, making it critical to how we perceive the world. Endowing machines with sight has been a challenging endeavor, but advancements in compute, algorithms, and data quality have made computer vision more accessible than ever before. From mobile cameras to industrial mechanic lenses, biological labs to hospital imaging, and self-driving cars to security cameras, data in pixel format is one of the most valuable types of data for consumers and companies. In this module, you will explore common CV applications, and you will learn how to build an end-to-end object detection model on Amazon SageMaker using NVIDIA GPUs.
Part: 4
Title:Natural Language Processing
Description:In this module, you will learn about the application of deep learning technologies to the problem of language understanding. What does it mean to understand languages? What is language modeling? What is the BERT language model, and why are such language models used in many popular services like search, office productivity software, and voice agents? Are NVIDIA GPUs a fast and cost-efficient platform to train and deploy NLP Models? In this section, you will find answers to all those questions and more. Whether you are an experienced ML engineer considering implementation or a developer wanting to learn to deploy a language understanding model like BERT quickly, this module is for you.
Machine learning (ML) projects can be complex, tedious, and time consuming. AWS and NVIDIA solve this challenge with fast, effective, and easy-to-use capabilities for your ML project. This course is designed for ML practitioners, including data scientists and developers, who have a working knowledge of machine learning workflows. In this course, you will gain hands-on experience on building, training, and deploying scalable machine learning models with Amazon SageMaker and Amazon EC2 instances powered by NVIDIA GPUs. Amazon SageMaker helps data scientists and developers prepare, build, train, and deploy high-quality ML models quickly by bringing together a broad set of capabilities purpose-built for ML. Amazon EC2 instances powered by NVIDIA GPUs along with NVIDIA software offer high performance GPU-optimized instances in the cloud for efficient model training and cost effective model inference hosting. In this course, you will first get an overview of Amazon SageMaker and NVIDIA GPUs. Then, you will get hands-on, by running a GPU powered Amazon SageMaker notebook instance. You will then learn how to prepare a dataset for model training, build a model, execute model training, and deploy and optimize the ML model. You will also learn, hands-on, how to apply this workflow for computer vision (CV) and natural language processing (NLP) use cases. After completing this course, you will be able to build, train, deploy, and optimize ML workflows with GPU acceleration in Amazon SageMaker and understand the key Amazon SageMaker services applicable to computer vision and NLP ML tasks.
