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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/machine-learning-duke
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课程名称：机器学习导论 课程概述：本课程将为您提供机器学习模型的基础知识，包括逻辑回归、多层感知器、卷积神经网络和自然语言处理等。此外，课程还将展示这些模型如何解决医疗诊断、图像识别和文本预测等各个行业的复杂问题。我们设计了实践练习，旨在为您提供在数据集上实施这些数据科学模型的动手经验。通过这些练习，您将学习如何使用PyTorch等开源库来实现机器学习算法，这些库被谷歌、NVIDIA、可口可乐、eBay、Snapchat、Uber等领先科技公司广泛使用。 课程大纲： 1. 机器学习简介 - 本模块将尽量减少数学内容，介绍机器学习的基本概念，包括逻辑回归和多层感知器（MLP）等基础模型，并讨论深度学习的概念及其与简单模型的关系。 2. 模型学习基础 - 本模块讨论深度网络学习的数学基础。首先，将学习如何将深度网络学习定义为一个数学函数的最小化问题，接着介绍验证方法以评估学习的深度网络的实际表现，并讨论经典的优化技术——梯度下降及其在深度网络学习中的应用。 3. 使用卷积神经网络进行图像分析 - 本周将涵盖模型训练、迁移学习和微调的基础知识。同时，对卷积神经网络（CNN）的直观理解进行详细讨论，以帮助学生掌握其概念。 4. 自然语言处理简介 - 本周讨论神经网络在自然语言处理（NLP）中的应用，从简单的神经模型到更复杂的模型。课程将介绍词嵌入的基本概念及其在模型学习和NLP应用中的应用，涉及多种神经NLP模型，包括递归神经网络及长短期记忆（LSTM）模型。 通过本课程，您将获得机器学习的基础理论和实际操作技能，为进一步学习和应用这一领域打下坚实的基础。
Name:Simple Introduction to Machine Learning
Description:The focus of this module is to introduce the concepts of machine learning with as little mathematics as possible. We will introduce basic concepts in machine learning, including logistic regression, a simple but widely employed machine learning (ML) method. Also covered is multilayered perceptron (MLP), a fundamental neural network. The concept of deep learning is discussed, and also related to simpler models.
Name:Basics of Model Learning
Description:In this module we will be discussing the mathematical basis of learning deep networks. We’ll first work through how we define the issue of learning deep networks as a minimization problem of a mathematical function. After defining our mathematical goal, we will introduce validation methods to estimate real-world performance of the learned deep networks. We will then discuss how gradient descent, a classical technique in optimization, can be used to achieve this mathematical goal. Finally, we will discuss both why and how stochastic gradient descent is used in practice to learn deep networks.
Name:Image Analysis with Convolutional Neural Networks
Description:This week will cover model training, as well as transfer learning and fine-tuning. In addition to learning the fundamentals of a CNN and how it is applied, careful discussion is provided on the intuition of the CNN, with the goal of providing a conceptual understanding.
Name:Introduction to Natural Language Processing
Description:This week will cover the application of neural networks to natural language processing (NLP), from simple neural models to the more complex. The fundamental concept of word embeddings is discussed, as well as how such methods are employed within model learning and usage for several NLP applications. A wide range of neural NLP models are also discussed, including recurrent neural networks, and specifically long short-term memory (LSTM) models.
This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc.) as well as demonstrate how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction. In addition, we have designed practice exercises that will give you hands-on experience implementing these data science models on data sets. These practice exercises will teach you how to implement machine learning algorithms with PyTorch, open source libraries used by leading tech companies in the machine learning field (e.g., Google, NVIDIA, CocaCola, eBay, Snapchat, Uber and many more).
