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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/neural-networks-random-forests
课程评论：没有评论
课程名称：神经网络与随机森林 课程概述：在本课程中，我们将基于基础模型的知识，探索高级人工智能技术。课程首先深入研究神经网络，从基础结构和属性开始。随后，我们将编写一些简单的神经网络模型，并学习避免过拟合、正则化及其他超参数技巧。在完成一个关于预测心脏病可能性的项目后，我们将转向随机森林。我们将详细描述这两种技术的差异及其不同的起源。最后，我们将完成一个使用随机森林预测患者相似性的项目。 课程大纲： 第一部分：深入神经网络 描述：在这一模块中，我们将详细探讨神经网络及其使用时的注意事项。我们将从添加层到我们的二层网络开始，研究不同选项及其影响。随后，我们将探讨一些更高级的Python库（如TensorFlow和Keras）中的神经网络。最后，我们将讨论其在科学中的影响及如何将模型应用于该领域。 第二部分：探索随机森林 描述：在这一模块中，我们将增强对随机森林及其在科学中应用的知识。我们将首先研究决策树及其如何作为独立模型运作。接下来，我们将讨论组合决策树以创建随机森林的影响。从这里，我们将谈论随机森林在回归和分类中的相似性与差异，最终通过一个项目预测物种来源来结束。 第三部分：期末项目：比较模型以预测花萼宽度 描述：在这个期末项目中，我们将比较一系列模型，以找出最能预测花萼宽度的模型。
Part: 1
Title: Deep Dive into Neural Networks
Description:In this module, we'll take a more detailed look into neural network and the considerations we should be having when using them. We'll start by adding layers to our 2-layer network, exploring the different options and their effects. Then, we'll explore some more advanced Python libraries for neural networks in TensorFlow and Keras. Finally, we'll discuss the implications to science and how to apply the models in the space.
Part: 2
Title:Exploring Random Forests
Description:In this module, we'll build up our knowledge of random forests and their uses in science. We'll start by exploring decision trees and how they operate as models in isolation. Next, we'll look at the impact of combining decision trees to create random forests. From here, we'll talk about the similarities and differences between regression and classification with random forests before concluding with a final project predicting species from lineage.
Part: 3
Title:Final Project: Comparing Models to Predict Sepal Width
Description:In this final project, we'll be comparing a suite of models to find the one that best predicts sepal width.
In this course, we will build on our knowledge of basic models and explore advanced AI techniques. We’ll start with a deep dive into neural networks, building our knowledge from the ground up by examining the structure and properties. Then we’ll code some simple neural network models and learn to avoid overfitting, regularization, and other hyper-parameter tricks. After a project predicting likelihood of heart disease given health characteristics, we’ll move to random forests. We’ll describe the differences between the two techniques and explore their differing origins in detail. Finally, we’ll complete a project predicting similarity between health patients using random forests.
