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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/advanced-learning-algorithms
课程评论：没有评论
课程名称：高级学习算法 课程概述：在机器学习专业化的第二门课程中，您将学习： - 使用TensorFlow构建并训练神经网络以进行多类分类 - 应用机器学习开发的最佳实践，使您的模型能够在现实世界中对数据和任务进行泛化 - 构建和使用决策树及树集成方法，包括随机森林和提升树 该机器学习专业化课程是由DeepLearning.AI与斯坦福在线合作创建的基础性在线项目。在这个适合初学者的课程中，您将学习机器学习的基本概念，以及如何利用这些技术构建现实世界的人工智能应用。 本专业化课程由人工智能先锋Andrew Ng教授授课，他在斯坦福大学进行过重要研究，并在谷歌大脑、百度和Landing.AI等机构开展了推动人工智能领域发展的开创性工作。 该课程包含三门课程，是Andrew开创性的机器学习课程的更新和扩展版本，评分为4.9（满分5分），自2012年推出以来已有超过480万学习者参与。 它广泛介绍了现代机器学习，包括监督学习（多元线性回归、逻辑回归、神经网络和决策树）、无监督学习（聚类、降维、推荐系统），以及在硅谷用于人工智能和机器学习创新的一些最佳实践（评估和调整模型、以数据为中心的方法来提高性能等）。 完成本专业化课程后，您将掌握关键理论概念，并获得快速有效地将机器学习应用于现实世界挑战的实践技能。如果您希望进入人工智能领域或在机器学习方面建立职业生涯，那么此机器学习专业化课程是您最好的起点。 课程大纲： 1. 神经网络：学习神经网络及其分类任务的应用，使用TensorFlow框架构建神经网络，并深入学习如何用Python从零开始编写自己的神经网络。 2. 神经网络训练：了解如何在TensorFlow中训练模型，学习各种激活函数及其应用，掌握多类分类的技巧，并探讨Adam优化器的优势。 3. 应用机器学习建议：学习机器学习生命周期中的最佳实践和模型调优的方法，以改善算法性能。 4. 决策树：了解常用的学习算法决策树及其变种，包括随机森林和提升树（XGBoost）。 通过本课程，您将获得必要的知识和技能，能够应对复杂的机器学习问题，并在人工智能领域取得成功。
Name:Neural Networks
Description:This week, you'll learn about neural networks and how to use them for classification tasks. You'll use the TensorFlow framework to build a neural network with just a few lines of code. Then, dive deeper by learning how to code up your own neural network in Python, "from scratch". Optionally, you can learn more about how neural network computations are implemented efficiently using parallel processing (vectorization).
Name:Neural network training
Description:This week, you'll learn how to train your model in TensorFlow, and also learn about other important activation functions (besides the sigmoid function), and where to use each type in a neural network. You'll also learn how to go beyond binary classification to multiclass classification (3 or more categories). Multiclass classification will introduce you to a new activation function and a new loss function. Optionally, you can also learn about the difference between multiclass classification and multi-label classification. You'll learn about the Adam optimizer, and why it's an improvement upon regular gradient descent for neural network training. Finally, you will get a brief introduction to other layer types besides the one you've seen thus far.
Name:Advice for applying machine learning
Description:This week you'll learn best practices for training and evaluating your learning algorithms to improve performance. This will cover a wide range of useful advice about the machine learning lifecycle, tuning your model, and also improving your training data.
Name:Decision trees
Description:This week, you'll learn about a practical and very commonly used learning algorithm the decision tree. You'll also learn about variations of the decision tree, including random forests and boosted trees (XGBoost).
In the second course of the Machine Learning Specialization, you will: • Build and train a neural network with TensorFlow to perform multi-class classification • Apply best practices for machine learning development so that your models generalize to data and tasks in the real world • Build and use decision trees and tree ensemble methods, including random forests and boosted trees The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications. This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.) By the end of this Specialization, you will have mastered key theoretical concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.
