Structuring Machine Learning Projects

开始时间: 04/22/2022 持续时间: Unknown

所在平台: CourseraArchive

课程类别: 计算机科学

大学或机构: CourseraNew

课程主页: https://www.coursera.org/archive/machine-learning-projects

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You will learn how to build a successful machine learning project. If you aspire to be a technical leader in AI, and know how to set direction for your team's work, this course will show you how. Much of this content has never been taught elsewhere, and is drawn from my experience building and shipping many deep learning products. This course also has two "flight simulators" that let you practice decision-making as a machine learning project leader. This provides "industry experience" that you might otherwise get only after years of ML work experience. After 2 weeks, you will: - Understand how to diagnose errors in a machine learning system, and - Be able to prioritize the most promising directions for reducing error - Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance - Know how to apply end-to-end learning, transfer learning, and multi-task learning I've seen teams waste months or years through not understanding the principles taught in this course. I hope this two week course will save you months of time. This is a standalone course, and you can take this so long as you have basic machine learning knowledge. This is the third course in the Deep Learning Specialization.

构建机器学习项目:您将学习如何构建成功的机器学习项目。如果您渴望成为AI的技术领导者,并且知道如何为团队的工作设定方向,那么本课程将向您展示如何。 这些内容中的许多内容从未在其他地方教过,并且取材自我在构建和交付许多深度学习产品方面的经验。本课程还具有两个“飞行模拟器”,使您可以作为机器学习项目负责人来实践决策。这提供了“行业经验”,否则您只有在多年的ML工作经验之后才能获得这些经验。 2周后,您将: -了解如何在机器学习系统中诊断错误,以及 -能够确定最有希望的方向以减少错误 -了解复杂的机器学习设置,例如不匹配的训练/测试集,并与人类水平的表现进行比较和/或超越 -知道如何应用端到端学习,迁移学习和多任务学习 我看到团队由于不了解本课程中讲授的原理而浪费了数月或数年。我希望这两周的课程将为您节省几个月的时间。 这是一门独立的课程,只要您具有基本的机器学习知识,就可以参加。这是深度学习专业课程中的第三门课程。

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You will learn how to build a successful machine learning project. If you aspire to be a technical l

课程标签

深度学习 机器学习 深度学习专项课程 机器学习项目 机器学习项目结构

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