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- 88% Complete Deep Learning In R With Keras & Others

Complete Deep Learning In R With Keras & Others

$9.99Track price

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8.4/10 (Our Score)
Product is rated as #171 in category Machine Learning

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…

Instructor Details

Hello. I am a PhD graduate from Cambridge University where I specialized in Tropical Ecology. I am also a Data Scientist on the side. As a part of my research I have to carry out extensive data analysis, including spatial data analysis.or this purpose I prefer to use a combination of freeware tools- R, QGIS and Python.I do most of my spatial data analysis work using R and QGIS. Apart from being free, these are very powerful tools for data visualization, processing and analysis. I also hold an MPhil degree in Geography and Environment from Oxford University. I have honed my statistical and data analysis skills through a number of MOOCs including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R based Machine Learning course offered by Standford online). In addition to spatial data analysis, I am also proficient in statistical analysis, machine learning and data mining. I also enjoy general programming, data visualization and web development. In addition to being a scientist and number cruncher, I am an avid traveler

Specification: Complete Deep Learning In R With Keras & Others

Duration

8 hours

Year

2019

Level

All

Certificate

Yes

Quizzes

No

30 reviews for Complete Deep Learning In R With Keras & Others

4.4 out of 5
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  1. Paul

    Just installation instructions so far

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  2. Monica Shah

    Very detailed course. One of the very few resources on deep learning in R. It covers keras

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  3. Lisbet Ivanov

    The best and most detailed r based Ai course out there. It covers all AI packages in detail

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  4. Anit Barma

    Fully Clear Deep Learning models with Keras for Regression and Classification tasks. I am enjoying it.

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  5. Jibon Saheb

    Learning has become very interesting and easy too. it can be organized better with chapters and appropriate headings.

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  6. Reggie John

    The only detailed AI course which shows practical applications in R

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  7. Moner Protishodh

    Amazing content and examples!! The course provided a good foundation to encourage me to further my interest in Deep learning.

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  8. Nilangona

    Very good explanation of key topic areas. Logical code and examples provided to reinforce the theory covered.

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  9. Perry Carter

    This is not the most polished presentation. Lots of slip ups.

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  10. Mojar Manus

    Please keep updating as R keeps adding new packages and some older might get deprecated. Thanks for this beautiful course

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  11. Kiyer Lagi

    Thanks Dr. Minerva Singh for designing an excellent course. I have learnt a lot within a very short period

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  12. Susan Tang

    Very detailed AI course

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  13. Dennis Andersen

    succint and informative : )

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  14. Sven van Hove

    Ein gro er Strau von Methoden und Anwendungen. Praxisnah beschrieben. Etwas mehr theoretischer Hintergrund und zur Interpretation von Input und Output w re sehr sinnvoll.

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  15. Jacob Lamkey

    Very useful information although the delivery could be better

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  16. Shama

    It was good for me.

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  17. Anya Rao

    One of the few deep learning in R resources out there

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  18. Woo Ling

    Its gathering pace after a slow start. It covers the most important deep learning packages in R

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  19. Omar Andres Carmona Cortes

    The code is not explained or discussed. I believe that at least the syntax should be presented.

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  20. Anthony Smith

    Very intense course on deep learning in R. It also covers keras

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  21. Robbie Jones

    It covers a lot of R packages for deep learning

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  22. Lindsay Hiebert

    Thank you. The course is very informative and the instructor is extremely knowledgeable and moves at a very quick pace to provide explanations and examples about the workflow and concept of machine learning. It may be of value to check many of the coding examples and samples for code and data that do not seem to work well out of the box for Complete Deep Learning in R with Keras and Others . for example missing data, code, samples, CV files and packages and commands that often do not run due to packages not being installed, code not being provided, or errors being generated during the working samples. for example errors such as these > library(keras) > > data dataset mnist() Error: Installation of TensorFlow not found. > library(keras) > d2 dataset mnist() Error: Installation of TensorFlow not found. train read csv(digit train.csv) Error: ‘digit train.csv’ does not exist in current working directory > modelNnet train(default ., + training, + method net, + metric ROC, + preProcess c(‘center’, ‘scale’), + trace FALSE, + tuneLength 1, + trControl fitControl) > modelNnet Neural Network 22500 samples 23 predictor 2 classes: ‘No’, ‘Yes’ Pre processing: centered (23), scaled (23) Resampling: Cross Validated (10 fold) Summary of sample sizes: 20250, 20250, 20249, 20251, 20249, 20251, … Resampling results: ROC Sens Spec 0.7210407 0.942078 0.38115 Tuning parameter ‘size’ was held constant at a value of 1 Tuning parameter ‘decay’ was held constant at a value of 0 > > summary(modelNnet) a 23 1 1 network with 26 weights options were entropy fitting b >h1 i1 >h1 i2 >h1 i3 >h1 i4 >h1 i5 >h1 i6 >h1 i7 >h1 i8 >h1 i9 >h1 3.63 0.43 0.14 0.08 0.11 0.13 2.74 0.24 0.16 0.37 i10 >h1 i11 >h1 i12 >h1 i13 >h1 i14 >h1 i15 >h1 i16 >h1 i17 >h1 i18 >h1 i19 >h1 0.12 0.23 0.25 0.14 0.47 0.53 0.43 0.18 1.14 2.76 i20 >h1 i21 >h1 i22 >h1 i23 >h1 0.63 0.14 0.98 0.09 b >o h1 >o 0.97 2.96 > > > predNnet predict(modelNnet, testing) > cmNnet confusionMatrix(predNnet, testing$default) Error: ‘data‘ and ‘reference‘ should be factors with the same levels. > cmNnet Error: object ‘cmNnet’ not found

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  23. Anil Kumar Pandey

    Very useful and informative

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  24. Rusty

    very well explained and immensely useful

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  25. sushma Pandey

    Informative course for me

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  26. Bryan Butler

    Overall, another good course for deep learning in R. Lots of different frameworks to work with.

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  27. Jojo Statis

    Very detailed resource on AI and deep learning in R

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  28. Mahender A

    Cover all the R based AI packages

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  29. Shivam Sarin

    Not really, the tutor only explains the codes not what happens behind it. I can find these codes online. Moreover the codes are different and incomplete for some lectures. For example check section 4 ann classification with mxnet. the code uploaded is different than what is shown.

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  30. Kaushal

    Informative

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    Complete Deep Learning In R With Keras & Others
    Complete Deep Learning In R With Keras & Others

    $9.99

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