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所在平台: Udemy |
课程主页: https://www.udemy.com/course/deep-learning-recommendation-algorithms-with-python/
课程评论：没有评论
课程名称：Python深度学习推荐算法 课程概述： 本课程将深入探讨成熟的推荐算法，首先基于邻域协同过滤，随后升级到更现代的技术，包括矩阵分解，甚至是使用人工神经网络的深度学习方法。在学习过程中，您将借助我们的丰富行业经验，了解在大规模和真实数据应用这些算法时所面临的现实挑战。您在Netflix首页、YouTube、Amazon等平台上看到的自动推荐，正是这些机器学习算法根据用户的独特兴趣，展示最适合个人的产品或内容。这些技术已成为众多顶尖技术公司的核心，了解其工作原理将使您变得非常有价值。 课程内容涵盖了从最早的协同过滤到最前沿的深度神经网络应用，以及现代机器学习技术在为每位用户推荐最佳项目方面的应用。参与本课程不应仅仅期望通过一步一步的编码学习。推荐系统是复杂的；没有固定的“食谱”可供参考，您需要理解各种算法，并在特定情况下选择何时使用每种算法。 本课程是高度实践性的；您将开发自己的框架，以评估和组合多种推荐算法，甚至使用Tensorflow构建自己的神经网络，以根据实际的用户电影评分生成推荐。课程中的编码练习采用Python编程语言进行。如果您对Python不熟悉，课程中会提供入门内容，但要求您具备一定的编程经验才能顺利学习。同时，课程还包含对深度学习的简要介绍，适合对人工智能领域新手，但您需要理解新的计算机算法。 这个综合性课程将带领您从协同过滤的早期阶段，一直到深度神经网络的前沿应用，以及为每位用户推荐最佳项目的现代机器学习技术。
We'll cover tried and true recommendation algorithms based on neighborhood-based collaborative filtering, and work our way up to more modern techniques including matrix factorization and even deep learning with artificial neural networks. Along the way, you'll learn from our extensive industry experience to understand the real-world challenges you'll encounter when applying these algorithms at large scale and with real-world data.You've seen automated recommendations everywhere - on Netflix's home page, on YouTube, and on Amazon as these machine learning algorithms learn about your unique interests, and show the best products or content for you as an individual. These technologies have become central to the largest, most prestigious tech employers out there, and by understanding how they work, you'll become very valuable to them.We'll cover tried and true recommendation algorithms based on neighborhood-based collaborative filtering, and work our way up to more modern techniques including matrix factorization and even deep learning with artificial neural networks.Recommender systems are complex; don't enroll in this course expecting a learn-to-code type of format. There's no recipe to follow on how to make a recommender system; you need to understand the different algorithms and how to choose when to apply each one for a given situation. We assume you already know how to code.However, this course is very hands-on; you'll develop your own framework for evaluating and combining many different recommendation algorithms together, and you'll even build your own neural networks using Tensorflow to generate recommendations from real-world movie ratings from real people.This comprehensive course takes you all the way from the early days of collaborative filtering, to bleeding-edge applications of deep neural networks and modern machine learning techniques for recommending the best items to every individual user.The coding exercises in this course use the Python programming language. We include an intro to Python if you're new to it, but you'll need some prior programming experience in order to use this course successfully. We also include a short introduction to deep learning if you are new to the field of artificial intelligence, but you'll need to be able to understand new computer algorithms.
