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所在平台: Udemy |
课程主页: https://www.udemy.com/course/advanced-neural-networks-in-r-a-practical-approach/
课程评论：没有评论
Coursera 课程 "Advanced Neural Networks in R - A Practical Approach" 总结： 本课程专注于使用 R 语言实践神经网络，是进入深度学习和人工智能领域的关键一步。课程强调实践而非复杂的数学理论，旨在帮助学员快速掌握核心概念并立即应用。 课程内容分为四个部分： 1. **多层感知器 (Multilayer Perceptrons - Beyond the Basics)**：学习如何利用多层感知器进行分类和回归预测，并通过 k-折交叉验证技术评估模型准确性，同时学习调整网络参数以优化预测。 2. **广义回归神经网络 (Generalized Regression Neural Networks)**：针对数值型响应变量的回归问题，演示如何使用此类网络预测汽车价值，并探讨如何通过调整平滑参数和使用 k-折交叉验证来改进模型。 3. **循环神经网络 (Recurrent Neural Networks)**：重点关注 Elman 和 Jordan 两种类型的循环神经网络，特别适用于时间序列建模和预测。课程将教授如何利用历史数据预测未来气温，并致力于构建高质量模型，以获得令人满意的预测准确性指标。 对于每种神经网络，课程都遵循相同的结构：简明易懂的理论介绍（避免复杂数学）、在 R 中训练网络的实践操作，以及在独立数据集上测试模型以确保预测能力。课程包含大量实践练习，让学员亲手应用所学知识。 本课程旨在让学员在短时间内成为神经网络专家，通过视频演示一步步教学，方便学员随时复现操作。课程承诺能够帮助学员掌握主要神经网络并使用 R 构建模型，提升职业技能。
Neural networks are powerful predictive tools that can be used for almost any machine learning problem with very good results. If you want to break into deep learning and artificial intelligence, learning neural networks is the first crucial step.This is why I'm inviting you to an exciting journey through the world of complex, state-of-the-art neural networks. In this course you will develop a strong understanding of the most utilized neural networks, suitable for both classification and regression problems.The mathematics behind neural networks is particularly complex, but you don't need to be a mathematician to take this course and fully benefit from it. We will not dive into complicated maths - our emphasis here is on practice. You will learn how to operate neural networks using the R program, how to build and train models and how to make predictions on new data.All the procedures are explained live, on real life data sets. So you will advance fast and be able to apply your knowledge immediately.This course contains four comprehensive sections.1. Multilayer Perceptrons - Beyond the BasicsLearn to use multilayer perceptrons to make predictions for both categorical and continuous variables. Moreover, learn how to test your models accuracy using the k-fold cross-validation technique and how improve predictions by manipulating various parameters of the network.3. Generalized Regression Neural NetworksIf you have to solve a regression problem (where your response variable is numeric), these networks can be very effective. We'll show how to predict a car value based on its technical characteristics and how to improve the prediction by controlling the smoothing parameter of our model. The k-fold cross-validation techniques will also be employed to identify better models.4. Recurrent Neural NetworksThese networks are useful for many prediction problems, but they are particularly valuable for time series modelling and forecasting. In this course we focus on two types of recurrent neural networks: Elman and Jordan. We are going to use them to predict future air temperatures based on historical data. Making truthful predictions on time series is generally very tough, but we will do our best to build good quality models and get satisfactory values for the prediction accuracy metrics.For each type of network, the presentation is structured as follows:a short, easy to understand theoretical introduction (without complex mathematics)how to train the network in Rhow to test the network to make sure that it does a good prediction job on independent data sets.For every neural network, a number of practical exercises are proposed. By doing these exercises you'll actually apply in practice what you have learned.This course is your opportunity to become a neural network expert in a few days only (literally). With my video lectures, you will find it very easy to master these major neural network and build them in R. Everything is shown live, step by step, so you can replicate any procedure at any time you need it.So click the "Enrol" button to get instant access to your course. It will surely get you some new, valuable skills. And, who knows, it could greatly enhance your future career.See you inside!
