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所在平台: Udemy |
课程主页: https://www.udemy.com/course/complete-deep-learning-in-r-with-keras-others/
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课程名称：R语言深度学习完全指南：Keras及其他工具 课程概述： 本课程是关于人工神经网络与深度学习的全面指南，专为希望在R语言中掌握数据科学的人士设计。通过学习本课程，您可以避免购买其他书籍或参加更多课程。如今，许多企业利用R语言来处理庞大的数据，掌握R中的神经网络和深度学习能够为您的职业生涯增添竞争优势。 授课教师Minerva Singh拥有牛津大学的地理与环境MPhil学位，并在剑桥大学完成了博士学位。她在数据科学领域有超过五年的实践经验，涵盖了从数据分析到学术发表的多个方面。本课程将深入探讨R语言中的数据科学特性，为您提供坚实的神经网络与深度学习基础。 课程内容包括： - 数据读取与清洗 - 实现强大的神经网络和深度学习算法 - 使用R的深度学习包，如h2o和MXNET - 深度神经网络（DNN）与卷积神经网络（CNN）的实现 - 使用Keras框架处理图像数据 - 真实数据的分类与回归应用，如信用卡欺诈和肿瘤数据 无须具备R、统计或机器学习的先前知识。本课程将以简单易懂的方式教授R数据科学的基础知识，帮助您用真实数据实现R语言的数据科学。课程中提供所有相关代码和数据，保证您能够在实际场景中运用所学知识。 欢迎立即加入我的课程！
YOUR COMPLETE GUIDE TO ARTIFICIAL NEURAL NETWORKS & DEEP LEARNING IN R: This course covers the main aspects of neural networks and deep learning. If you take this course, you can do away with taking other courses or buying books on R based data science. In this age of big data, companies across the globe use R to sift through the avalanche of information at their disposal. By becoming proficient in neural networks and deep learning in R, you can give your company a competitive edge and boost your career to the next level!LEARN FROM AN EXPERT DATA SCIENTIST:My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University. I have +5 years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals.Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic. This course will give you a robust grounding in the main aspects of practical neural networks and deep learning. Unlike other R instructors, I dig deep into the data science features of R and give you a one-of-a-kind grounding in data science.You will go all the way from carrying out data reading & cleaning to to finally implementing powerful neural networks and deep learning algorithms and evaluating their performance using R.Among other things:You will be introduced to powerful R-based deep learning packages such as h2o and MXNET. You will be introduced to deep neural networks (DNN), convolution neural networks (CNN) and unsupervised methods. You will learn how to implement convolutional neural networks (CNN)s on imagery data using the Keras frameworkYou will learn to apply these frameworks to real life data including credit card fraud data, tumor data, images among others for classification and regression applications. With this course, you'll have the keys to the entire R Neural Networks and Deep Learning Kingdom!NO PRIOR R OR STATISTICS/MACHINE LEARNING KNOWLEDGE IS REQUIRED:You'll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R. My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real-life.After taking this course, you'll easily use data science packages like caret, h2o, mxnet, keras to implement novel deep learning techniques in R. You will get your hands dirty with real life data, including real-life imagery data which you will learn to pre-process and model You'll even understand the underlying concepts to understand what algorithms and methods are best suited for your data. We will also work with real data and you will have access to all the code and data used in the course. JOIN MY COURSE NOW!
