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所在平台: Udemy |
课程主页: https://www.udemy.com/course/unleashing-unlabelled-data-self-supervised-learning/
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课程名称：解锁未标记数据：自监督学习 课程概述：自监督机器学习是一种从未标记数据中学习的范式，无需明确的人为标记。它涉及创建替代性或前置任务，模型通过这些任务训练以解决原始数据的问题。通过专注于这些任务，模型学习捕捉潜在的模式和结构，使其能够发现有用的表示。自监督学习利用了丰富的未标记数据，减少了人工标注的需求，并生成丰富且可转移的表示。它在多个领域取得了成功，为利用未标记数据提取有意义的信息提供了有前景的方法。 本课程适合自监督机器学习的新手，您将学习如何使用强大的云端Python环境Google Colab，流利地部署基于数据科学的商业智能解决方案。具体内容包括： - 理解自监督机器学习及其重要性 - 在Google Colab中实现Python数据科学框架的主要方面 - 通过使用重要的AI软件包，包括H2O和Keras，实施常见的数据科学框架 - 使用常见的自监督机器学习技术从未标记数据中学习 - 执行重要的AI任务，包括图像去噪和异常检测 此外，您将获得我持续的支持，确保您充分利用投资价值！ 为何选择我的课程？我的课程为开展实际的自监督机器学习奠定基础。参加此课程是您数据科学之旅向前迈出重要一步，有助于您成为利用未标记数据提取洞察和识别趋势的专家。 我拥有牛津大学的地理与环境硕士学位，并在剑桥大学完成了数据科学密集型博士学位（热带生态与保护）。我有多年的经验分析来自不同来源的实际数据，发表国际同行评审期刊的论文，并进行数据科学咨询工作。 请立即注册！
Self-supervised machine learning is a paradigm that learns from unlabeled data without explicit human labelling. It involves creating surrogate or pretext tasks that the model is trained to solve using the raw data. By focusing on these tasks, the model learns to capture underlying patterns and structures, enabling it to discover useful representations. Self-supervised learning benefits from abundant unlabeled data reduces the need for manual annotation, and produces rich and transferable representations. It has found success in various arenas, offering a promising approach to leverage unlabeled data for extracting meaningful information without relying on external labels.IF YOU ARE A NEWCOMER TO SELF-SUPERVISED MACHINE LEARNING, ENROLL IN MY LATEST COURSE ON HOW TO LEARN ALL ABOUT THIS LATEST ADVANCEMENT IN ARTIFICIAL INTELLIGENCEThis course will help you gain fluency in deploying data science-based BI solutions using a powerful clouded based python environment called GoogleColab. Specifically, you will Learn the main aspects of implementing a Python data science framework within Google Colab.Learn what self-supervised machine learning is and its importanceLearn to implement the common data science frameworks and work with important AI packages, including H2O and KerasUse common self-supervised machine learning techniques to learn from unlabelled dataCarry out important AI tasks, including denoising images and anomaly detectionIn addition to all the above, you'll have MY CONTINUOUS SUPPORT to ensure you get the most value out of your investment!ENROLL NOW:)Why Should You Take My Course?My course provides a foundation to conduct PRACTICAL, real-life self-supervised machine learning By taking this course, you are taking a significant step forward in your data science journey to become an expert in harnessing the power of unlabelled data for deriving insights and identifying trends.I have an MPhil (Geography and Environment) from the University of Oxford, UK. I also completed a data science intense PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience analyzing real-life data from different sources, producing publications for international peer-reviewed journals and undertaking data science consultancy work. In addition to all the above, you'll have MY CONTINUOUS SUPPORT to ensure you get the most value out of your investment!ENROLL NOW:)
