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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-variational-autoencoders/
课程评论：没有评论
**课程名称：** 机器学习：变分自编码器导论 **课程概述：** 随着数据量的爆炸式增长，无监督学习算法正变得越来越高效和有价值。掌握这些技术的公司将在人工智能领域获得显著的竞争优势。本课程旨在教授学员如何利用未标记的数据集进行机器学习。我们将重点关注自编码器（Autoencoders）和变分自编码器（Variational Autoencoders），并深入探讨它们在无监督学习中的训练方法，这在处理大数据时代尤为重要。 **本课程使用 Python 语言教学，要求学员具备基础的编程能力。** 推荐学员拥有基本的优化（特别是梯度优化）和机器学习知识。 **课程内容：** * 自编码器的概念及其在 Python 中的实现 * 变分自编码器的概念及其在 Python 中的实现 * 无监督学习 * 生成模型 * 通过实践学习 PyTorch * 实现一篇科学机器学习论文（"Auto-Encoding Variational Bayes"） **立即加入，开启您的无监督机器学习之旅！**
In a world of increasingly accessible data, unsupervised learning algorithms are becoming more and more efficient and profitable. Companies that understand this will soon have a competitive advantage over those who are slow to jump on the artificial intelligence bandwagon. As a result, developers with Machine Learning and Deep Learning skills are increasingly in demand and have gold on their hands. In this course, we will see how to take advantage of a raw dataset, without any labels. In particular, we will focus exclusively on Autoencoders and Variational Autoencoders and see how they can be trained in an unsupervised way, making them particularly attractive in the era of Big Data. This course, taught using the Python programming language, requires basic programming skills. If you don't have the required foundation, I recommend that you brush up on your skills by taking a crash course in programming. Also, it is best to have basic knowledge of optimization (we will use gradient optimization) and machine learning.Concepts covered: Autoencoders and their implementation in Python Variational Autoencoders and their implementations in PythonUnsupervised Learning Generative models PyTorch through practice The implementation of a scientific ML paper (Auto-Encoding Variational Bayes) Don't wait any longer before jumping into the world of unsupervised Machine Learning!
