'Learn and Apply Artificial Neural Networks (aka Deep Learning) with Keras/Tensorflow in R/RStudio, Efficiently',
Using complete prewritten codes and your own data, your own data, and data.table package.
Based on:
- https://github.com/jjallaire/deep-learning-with-r-notebooks
- https://keras.rstudio.com (= https://tensorflow.rstudio.com/keras)
- https://www.manning.com/books/deep-learning-with-r
- everything else found useful on the Web,
- and my own work on ANN back from 1995-2005 on PINN :)
Notes:
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Latest version of RStudio is always recommended (Presently, Version 1.1.447 – 2018 )
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All codes are retrieved from original sources, simplified, merged and directly runnable from RStudio
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The order of lessons is recommended by indices: e.g. 1-1-1 goes prior to 1-3-1. [.] are optional.
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# .... ####comments are used for quick navigation from one example/section to another within RStudio IDE. -
# >>> ... <<< ####indicate Main sections -
Data to play with (traffic, favourite readings) are provided, inc. very small sets to run fast.
See also:
- 'Learn and Apply interactive programming and presentations with RStudio Shiny'.
- 'Learn and Apply Text Analysis in R, Efficiently'
Contents: ####
- Start here: https://keras.rstudio.com/index.html (which is the same as https://tensorflow.rstudio.com/keras) Then, as instructed there go to. # Learning More:
1-2. Guide to the Sequential Model - https://keras.rstudio.com/articles/sequential_model.html Then, as instructed there go to # Examples:
[1-2-1]. CIFAR10 small images classification - https://keras.rstudio.com/articles/examples/cifar10_cnn.html 1-2-2. IMDB movie review sentiment classification - https://keras.rstudio.com/articles/examples/imdb_cnn_lstm.html 1-2-3. Reuters newswires topic classification - https://keras.rstudio.com/articles/examples/reuters_mlp.html 1-2-4. MNIST handwritten digits classification - https://keras.rstudio.com/articles/examples/mnist_mlp.html
1-3. Guide to the Functional API - https://keras.rstudio.com/articles/functional_api.html [1-4]. Frequently Asked Questions - https://keras.rstudio.com/articles/faq.html 1-1. Training Visualization - https://keras.rstudio.com/articles/training_visualization.html
Other files:
- DLwR-s6.1-RNN-for-text.R
- DLwR-s6.1-RNN-forSequences.R
- DLwR-s3-IMDB_sentimentBinary+wiresClassification+housepriceReression.R
Libraries used (and highly recommended for efficient programming):
library(ggplot2);library(data.table); library(magrittr); library(lubridate); library(stringr)