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所在平台: Udemy |
课程主页: https://www.udemy.com/course/building-recommender-systems-with-machine-learning-and-ai/
课程评论：没有评论
课程名称：使用机器学习和人工智能构建推荐系统 课程概述：本课程更新了神经协同过滤（NCF）、Tensorflow 推荐系统（TFRS）和生成对抗网络（GANs）在推荐中的应用。由亚马逊领域的先驱 Frank Kane 主讲，他在亚马逊工作了超过九年，负责和管理了多款个性化产品推荐系统的开发。你无时无刻不在接触自动推荐，无论是在 Netflix 的主页、YouTube 还是亚马逊，这些机器学习算法会根据你的独特兴趣，展示最适合你的产品或内容。这些技术已成为最大、最具声望的科技公司所依赖的核心，因此了解如何运作将使你在职场中非常有价值。 课程内容将涵盖成熟的基于邻域的协同过滤推荐算法，并逐步深入到基于矩阵分解和深度学习的现代技术中。在学习过程中，Frank 将分享他丰富的行业经验，帮助学员理解在大规模应用这些算法及处理真实数据时所面临的实际挑战。 课程为实践导向，学员将开发自己的框架来评估和结合多种推荐算法，甚至使用 Tensorflow 构建自己的神经网络，从真实的电影评分中生成推荐。主要内容包括： - 构建推荐引擎 - 评估推荐系统 - 使用项目属性的基于内容的过滤 - 基于用户、项目和 KNN CF 的邻域协同过滤 - 包括矩阵分解和 SVD 的模型基方法 - 将深度学习、人工智能及人工神经网络应用于推荐 - 使用 Tensorflow（TFRS）和亚马逊个性化的最新框架 - 基于递归神经网络的会话推荐 - 使用神经协同过滤构建现代推荐系统 - 使用 Apache Spark 机器学习、亚马逊 DSSTNE 深度学习和 AWS SageMaker 进行大规模数据集扩展 - 推荐系统的实际挑战与解决方案 - 来自 YouTube 和 Netflix 的案例研究 - 构建混合、集成推荐系统 - 提供最新研究的“尖端警报” 本课程全面涵盖了从协同过滤早期发展到利用深度神经网络和现代机器学习技术为每个用户推荐最佳项目的应用。课程的编码练习使用 Python 编程语言，虽然我们会为新手提供 Python 入门，但学员需要具备一定的编程经验以顺利进行本课程。学习编程并不是课程的重点，我们主要关注算法及其实际示例。此外，我们还会为对人工智能新手提供简短的深度学习介绍，但学员需能够理解新的计算机算法。课程还包括高质量的英文关闭字幕，以帮助学员顺利学习。 期待不久后在课程中见到你！
Updated with Neural Collaborative Filtering (NCF), Tensorflow Recommenders (TFRS) and Generative Adversarial Networks for recommendations (GANs)Learn how to build machine learning recommendation systems from one of Amazon's pioneers in the field. Frank Kane spent over nine years at Amazon, where he managed and led the development of many of Amazon's personalized product recommendation systems. 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. Along the way, you'll learn from Frank's extensive industry experience to understand the real-world challenges you'll encounter when applying these algorithms at large scale and with real-world data.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. We'll cover:Building a recommendation engineEvaluating recommender systemsContent-based filtering using item attributesNeighborhood-based collaborative filtering with user-based, item-based, and KNN CFModel-based methods including matrix factorization and SVDApplying deep learning, AI, and artificial neural networks to recommendationsUsing the latest frameworks from Tensorflow (TFRS) and Amazon Personalize.Session-based recommendations with recursive neural networksBuilding modern recommenders with neural collaborative filteringScaling to massive data sets with Apache Spark machine learning, Amazon DSSTNE deep learning, and AWS SageMaker with factorization machinesReal-world challenges and solutions with recommender systemsCase studies from YouTube and NetflixBuilding hybrid, ensemble recommenders"Bleeding edge alerts" covering the latest research in the field of recommender systemsThis 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. Learning how to code is not the focus of this course; it's the algorithms we're primarily trying to teach, along with practical examples. 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.High-quality, hand-edited English closed captions are included to help you follow along.I hope to see you in the course soon!
