Neural Networks for Time Series Forecasting with R An Intuitive Step by Step Blueprint for Beginners N D Lewis 9781544752952 Books
Download As PDF : Neural Networks for Time Series Forecasting with R An Intuitive Step by Step Blueprint for Beginners N D Lewis 9781544752952 Books
Finally, A Blueprint for Neural Network Time Series Forecasting with R!
Neural Networks for Time Series Forecasting with R offers a practical tutorial that uses hands-on examples to step through real-world applications using clear and practical case studies. Through this process it takes you on a gentle, fun and unhurried journey to creating neural network models for time series forecasting with R. Whether you are new to data science or a veteran, this book offers a powerful set of tools for quickly and easily gaining insight from your data using R.NO EXPERIENCE REQUIRED This book uses plain language rather than a ton of equations; I’m assuming you never did like linear algebra, don’t want to see things derived, dislike complicated computer code, and you’re here because you want to try neural networks for time series forecasting for yourself.
YOUR PERSONAL BLUE PRINT Through a simple to follow step by step process, you will learn how to build neural network time series forecasting models using R. Once you have mastered the process, it will be easy for you to translate your knowledge into your own powerful applications.
THIS BOOK IS FOR YOU IF YOU WANT
TAKE THE SHORTCUT This guide was written for people just like you. Individuals who want to get up to speed as quickly as possible. In this book you will learn how to
YOU'LL LEARN HOW TO
For each neural network model, every step in the process is detailed, from preparing the data for analysis, to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks. Using plain language, this book offers a simple, intuitive, practical, non-mathematical, easy to follow guide to the most successful ideas, outstanding techniques and usable solutions available using R.
Everything you need to get started is contained within this book. Neural Networks for Time Series Forecasting with R is your very own hands on practical, tactical, easy to follow guide to mastery.
Buy this book today and accelerate your progress!
Neural Networks for Time Series Forecasting with R An Intuitive Step by Step Blueprint for Beginners N D Lewis 9781544752952 Books
If you do understand something in NN, this book is not for you. If you want to understand NN, still this book is not for you (book mostly about R).This book mostly as monkey instruction: do that, type that and you will get that. This book for someone who wants to try R (not learn) with NN (not learn).
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Tags : Neural Networks for Time Series Forecasting with R: An Intuitive Step by Step Blueprint for Beginners [N D Lewis] on Amazon.com. *FREE* shipping on qualifying offers. <h2> Finally, A Blueprint for Neural Network Time Series Forecasting with R!</h2> <b> Neural Networks for Time Series Forecasting with R</b> offers a practical tutorial that uses hands-on examples to step through real-world applications using clear and practical case studies. Through this process it takes you on a gentle,N D Lewis,Neural Networks for Time Series Forecasting with R: An Intuitive Step by Step Blueprint for Beginners,CreateSpace Independent Publishing Platform,1544752954,COMPUTERS Neural Networks
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Neural Networks for Time Series Forecasting with R An Intuitive Step by Step Blueprint for Beginners N D Lewis 9781544752952 Books Reviews
This book has several interesting applications for neural networks, specifically withnpackages MXNet and RNN. The instructions are clear, cut, and dry, to the point and error free. The only problem is that the title is incorrect. Do not expect to learn how to forecast out of sample. No instructions are present in this book, except for an an R package (TSDyn?), which is dated. This book is about backtesting, not forecasting.
I Feel the last part of forecasting is mission.
This text brought me up to speed on alternative neural network approaches that I was not familiar with. Since Tensorflow needs Python to operate rather than running on top of R, it of course was not covered. Otherwise, the coverage was thorough and easy to follow.
on page 106
lab_3<−RDts1 [302301,]
should be
lab_3<−RDts1 [202301,]
The book is to be used as a very basic overview of the few neural network methods and as an R code example booklet. Although the author emphasizes that no theory is covered, it would be good if the methods overviews were a bit more detailed and the code examples a bit more explained. But overall, it gives what it says.
No one explains these concepts better than this author; no one makes NN for Time Series more practical. And, for the price, the author is practically giving it away.
So, I get the book, and then, the version for my android smart phone and I still have enough lunch money in my pocket! This means that I can whip out the examples and look up the relevant R codes anywhere any time on my smartphone, or refer to it instead of the book when I am trying the examples in the book while working on my desktop PC.
The examples in the book are easy to follow; the results are right on the mark. I also used my own data as I was following along, and it was even more rewarding.
In the Preface, the author made a promise. When I was through the book the first time, I considered that promise fulfilled.
Did I add that the author was quite helpful when I had questions? He actually addressed those issues to my satisfaction. He really cares!
I will purchase whatever he puts out in R.
This is exactly what I was looking for; a cook-book on how to implement neural nets for time series forecasting with R. Remember, this is neither a time series book, nor an R book. It is also light on mathematical theory of neural nets, although provides necessary background on neural nets used for time series forecasting. The author highlights and explains major types of neural nets used in forecasting and gives several worked examples.
Given the scarcity of resources on practical use of nnets in forecasting, this book is a really useful resource.
Why three stars then? Well, there are few major issues with this print. First of all, there are lots of typos in the text, especially in the R code which make it hard and sometimes impossible to replicate the examples (isn't that the whole point here?). Second, the book has been edited poorly (disproportional charts, text sizes, etc). The R code used for data wrangling is somewhat written in a basic, inefficient and unnecessary lengthy fashion. Overall, I like this book and think it's useful for practitioners.
If you do understand something in NN, this book is not for you. If you want to understand NN, still this book is not for you (book mostly about R).
This book mostly as monkey instruction do that, type that and you will get that. This book for someone who wants to try R (not learn) with NN (not learn).
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