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- <!DOCTYPE html>
- <html>
- <head>
- <title>Deep Learning in R</title>
- <meta charset="utf-8">
- <meta name="author" content="metya" />
- <meta name="date" content="2018-12-19" />
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- class: center, middle, inverse, title-slide
- # Deep Learning in R
- ## ░<br/>Обзор фреймворков с примерами
- ### metya
- ### 2018-12-19
- ---
- class: center, middle
- background-color: #8d6e63
- #Disclaimer
- Цель доклада не дать понимание, что такое глубокое обучение и детально разобрать как работать с ним и обучать современные модели, а скорее показать, как просто можно начать тем, кто давно хотел и чесались руки, но все было никак не взяться
- ---
- # Deep Learning
- ## Что это?
- --
- * Когда у нас есть исскуственная нейронная сеть
- --
- * Когда скрытых слоев в этой сети больше чем два
- --
- 
- .footnotes[[1] https://machinelearningmastery.com/what-is-deep-learning/]
- ---
- ## Как это математически
- 
- ???
- На самом деле это конечно самый простой юнит, самый базовый.
- ---
- background-image: url(https://3qeqpr26caki16dnhd19sv6by6v-wpengine.netdna-ssl.com/wp-content/uploads/2016/08/Why-Deep-Learning-1024x742.png)
- ???
- Image credit: [Andrew Ng](http://www.slideshare.net/ExtractConf)
- ---
- class: inverse, center, middle, title-slide
- # Frameworks
- ---
- 
- .footnotes[
- [1] https://towardsdatascience.com/deep-learning-framework-power-scores-2018-23607ddf297a
- ]
- ---
- ## Нас интересуют только те, что есть в R через API
- --
- * ###TensorFlow
- --
- * ###theano
- --
- * ###Keras
- --
- * ###CNTK
- --
- * ###MXNet
- --
- * ###ONNX
- ---
- ## Есть еше несколько пакетов
- * darch (removed from cran)
- * deepnet
- * deepr
- * H2O (interface) ([Tutorial](https://htmlpreview.github.io/?https://github.com/ledell/sldm4-h2o/blob/master/sldm4-deeplearning-h2o.html))
- ???
- Вода это по большей части МЛ фреймворк, с недавних пор, где появился модуль про глубокое обучение. Есть неплохой туториал для р пакета. Умеет в поиск гиперпараметров, кроссвалидацию и прочие нужные для МЛ штуки для сеток, очевидно это работает только для маленьких сетей)
- Но они р специфичны, кроме воды, и соотвественно медленные, да и умеют довольно мало. Новые годные архитектуры сетей туда не имплементированы.
- ---
- 
- https://www.tensorflow.org/
- https://tensorflow.rstudio.com/
- - Делает Google
- - Самый популярный, имеет тучу туториалов и книг
- - Имеет самый большой спрос у продакшн систем
- - Имеет API во множество языков
- - Имеет статический граф вычислений, что бывает неудобно, зато оптимизированно
- - Примерно с лета имеет фичу **eager execution**, которая почти нивелирует это неудобство. Но почти не считается
- - Доступен в R как самостоятельно, так и как бэкэнд Keras
- ---
- 
- http://www.deeplearning.net/software/theano/
- - Делался силами университета Монреаль с 2007
- - Один из самый старых фреймворков, но почти почил в забытьи
- - Придумали идею абстракции вычислительных графов (статических) для оптимизации и вычисления нейронных сетей
- - В R доступен как бэкенд через Keras
- ---
- 
- https://cntk.ai/
- - Делается силами Майкрософт
- - Имеет половинчатые динамические вычислительные графы (на самом деле динамические тензоры скорее)
- - Доступен как бэкенд Keras так и как самостоятельный бэкенд с биндингами в R через reticulate package, что значит нужно иметь python версию фреймворка
- ---
- 
- https://keras.io/
- https://keras.rstudio.com/
- https://tensorflow.rstudio.com/keras/
- - Высокоуровневый фреймворк над другими такими бэкэндами как Theano, CNTK, Tensorflow, и еще некоторые на подходе
- - Делается Франсуа Шолле, который написал книгу Deep Learning in R
- - Очень простой код
- - Один и тот же код работает на разных бэкендах, что теоретически может быть полезно (нет)
- - Есть очень много блоков нейросетей из современных State-of-the-Art работ
- - Нивелирует боль статических вычислительных графов (не совсем)
- - Уже давно дефолтом поставляется вместе с TensorFlow как его часть, но можно использовать и отдельно
- ---
- <img src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo-m/mxnetR.png" style=width:30% />
- https://mxnet.apache.org/
- https://github.com/apache/incubator-mxnet/tree/master/R-package
- - Является проектом Apache
- - Сочетает в себе динамические и статические графы
- - Тоже имеет зоопарк предобученных моделей
- - Как и TensorFlow поддерживается многими языками, что может быть очень полезно
- - Довольно разумный и хороший фреймворк, непонятно, почему не пользуется популярностью
- ---
- 
- https://onnx.ai/
- https://onnx.ai/onnx-r/
- - Предоставляет открытый формат представления вычислительных графов, чтобы можно было обмениваться запускать одни и теже, экспортированные в этот формат, модели с помощью разных фреймворков и своего рантайма
- - Можно работать из R
- - Изначально делался Microsoft вместе с Facebook
- - Поддерживает кучу фреймворков нативно и конвертацию в ML и TF, Keras
- ---
- class: inverse, middle, center
- # Deep Learning with MXNet
- ---
- ## Установка
- В Windows и MacOS в R
- ```r
- # Windows and MacOs
- cran <- getOption("repos")
- cran["dmlc"] <- "https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/R/CRAN/GPU/cu92"
- options(repos = cran)
- install.packages("mxnet")
- ```
- Linux bash
- ```bash
- # On linux
- git clone --recursive https://github.com/apache/incubator-mxnet.git mxnet
- cd mxnet/docs/install
- ./install_mxnet_ubuntu_python.sh
- ./install_mxnet_ubuntu_r.sh
- cd incubator-mxnet
- make rpkg
- ```
- ---
- ## Загрузка и обработка данных
- ```r
- df <- readRDS('data.rds')
- set.seed(100) #set seed to reproduce results
- ```
- ```r
- #transform and split train on x and y
- train_ind <- sample(1:77, 60) # random split data
- x_train <- as.matrix(df[train_ind, 2:8]) # train data
- y_train <- unlist(df[train_ind, 9]) # train labels
- x_val <- as.matrix(df[-train_ind, 2:8]) # test validation data
- y_val <- unlist(df[-train_ind, 9]) # validation labels
- ```
- ---
- ## Задания архитектуры сети
- ```r
- require(mxnet)
- # define graph
- data <- mx.symbol.Variable("data") # define variable node
- fc1 <- mx.symbol.FullyConnected(data, num_hidden = 1) # define one layer perceptron
- linreg <- mx.symbol.LinearRegressionOutput(fc1) # output node
- # define learing parameters
- initializer <- mx.init.normal(sd = 0.1)
- optimizer <- mx.opt.create("sgd",
- learning.rate = 1e-6,
- momentum = 0.9)
- # define logger for logging train proccess
- logger <- mx.metric.logger()
- epoch.end.callback <- mx.callback.log.train.metric(
- period = 4, # number of batches when metrics call
- logger = logger)
- # num of epoch
- n_epoch <- 20
- ```
- ---
- ## Построим граф модели
- ```r
- # plot our model
- graph.viz(linreg)
- ```
- <div id="htmlwidget-33d7656478955a07fef5" style="width:504px;height:288px;" class="grViz html-widget"></div>
- <script type="application/json" data-for="htmlwidget-33d7656478955a07fef5">{"x":{"diagram":"digraph {\n\ngraph [layout = \"dot\",\n rankdir = \"TD\"]\n\n\n\n \"1\" [label = \"data\ndata\", shape = \"oval\", penwidth = \"2\", color = \"#8dd3c7\", style = \"filled\", fontcolor = \"black\", fillcolor = \"#8DD3C7FF\"] \n \"2\" [label = \"FullyConnected\nfullyconnected9\n1\", shape = \"box\", penwidth = \"2\", color = \"#fb8072\", style = \"filled\", fontcolor = \"black\", fillcolor = \"#FB8072FF\"] \n \"3\" [label = \"LinearRegressionOutput\nlinearregressionoutput9\", shape = \"box\", penwidth = \"2\", color = \"#b3de69\", style = \"filled\", fontcolor = \"black\", fillcolor = \"#B3DE69FF\"] \n\"1\"->\"2\" [color = \"black\", fontcolor = \"black\"] \n\"2\"->\"3\" [color = \"black\", fontcolor = \"black\"] \n}","config":{"engine":"dot","options":null}},"evals":[],"jsHooks":[]}</script>
- ---
- ## Обучим
- ```r
- model <- mx.model.FeedForward.create(
- symbol = linreg, # our model
- X = x_train, # our data
- y = y_train, # our label
- ctx = mx.cpu(), # engine
- num.round = n_epoch, # number of epoch
- initializer = initializer, # inizialize weigths
- optimizer = optimizer, # sgd optimizer
- eval.data = list(data = x_val, label = y_val), # evaluation on evey epoch
- eval.metric = mx.metric.rmse, # metric
- array.batch.size = 15,
- epoch.end.callback = epoch.end.callback) # logger
- ```
- 
- ---
- ## Построим кривую обучения
- ```r
- rmse_log <- data.frame(RMSE = c(logger$train, logger$eval), dataset = c(rep("train", length(logger$train)), rep("val", length(logger$eval))),epoch = 1:n_epoch)
- library(ggplot2)
- ggplot(rmse_log, aes(epoch, RMSE, group = dataset, colour = dataset)) + geom_point() + geom_line()
- ```
- <!-- -->
- ---
- class: inverse, center, middle
- # Deep Learning with Keras
- ---
- ## Установка
- ```r
- install.packages("keras")
- keras::install_keras(tensorflow = 'gpu')
- ```
- ### Загрузка нужных нам пакетов
- ```r
- require(keras) # Neural Networks
- require(tidyverse) # Data cleaning / Visualization
- require(knitr) # Table printing
- require(rmarkdown) # Misc. output utilities
- require(ggridges) # Visualization
- ```
- ---
- ## Загрузка данных
- ```r
- activityLabels <- read.table("Deep_Learning_in_R_files/HAPT Data Set/activity_labels.txt",
- col.names = c("number", "label"))
- activityLabels %>% kable(align = c("c", "l"))
- ```
- number label
- -------- -------------------
- 1 WALKING
- 2 WALKING_UPSTAIRS
- 3 WALKING_DOWNSTAIRS
- 4 SITTING
- 5 STANDING
- 6 LAYING
- 7 STAND_TO_SIT
- 8 SIT_TO_STAND
- 9 SIT_TO_LIE
- 10 LIE_TO_SIT
- 11 STAND_TO_LIE
- 12 LIE_TO_STAND
- ---
- ```r
- labels <- read.table("Deep_Learning_in_R_files/HAPT Data Set/RawData/labels.txt",
- col.names = c("experiment", "userId", "activity", "startPos", "endPos"))
- dataFiles <- list.files("Deep_Learning_in_R_files/HAPT Data Set/RawData")
- labels %>%
- head(50) %>%
- paged_table()
- ```
- <div data-pagedtable="false">
- <script data-pagedtable-source type="application/json">
- {"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["experiment"],"name":[1],"type":["int"],"align":["right"]},{"label":["userId"],"name":[2],"type":["int"],"align":["right"]},{"label":["activity"],"name":[3],"type":["int"],"align":["right"]},{"label":["startPos"],"name":[4],"type":["int"],"align":["right"]},{"label":["endPos"],"name":[5],"type":["int"],"align":["right"]}],"data":[{"1":"1","2":"1","3":"5","4":"250","5":"1232","_rn_":"1"},{"1":"1","2":"1","3":"7","4":"1233","5":"1392","_rn_":"2"},{"1":"1","2":"1","3":"4","4":"1393","5":"2194","_rn_":"3"},{"1":"1","2":"1","3":"8","4":"2195","5":"2359","_rn_":"4"},{"1":"1","2":"1","3":"5","4":"2360","5":"3374","_rn_":"5"},{"1":"1","2":"1","3":"11","4":"3375","5":"3662","_rn_":"6"},{"1":"1","2":"1","3":"6","4":"3663","5":"4538","_rn_":"7"},{"1":"1","2":"1","3":"10","4":"4539","5":"4735","_rn_":"8"},{"1":"1","2":"1","3":"4","4":"4736","5":"5667","_rn_":"9"},{"1":"1","2":"1","3":"9","4":"5668","5":"5859","_rn_":"10"},{"1":"1","2":"1","3":"6","4":"5860","5":"6786","_rn_":"11"},{"1":"1","2":"1","3":"12","4":"6787","5":"6977","_rn_":"12"},{"1":"1","2":"1","3":"1","4":"7496","5":"8078","_rn_":"13"},{"1":"1","2":"1","3":"1","4":"8356","5":"9250","_rn_":"14"},{"1":"1","2":"1","3":"1","4":"9657","5":"10567","_rn_":"15"},{"1":"1","2":"1","3":"1","4":"10750","5":"11714","_rn_":"16"},{"1":"1","2":"1","3":"3","4":"13191","5":"13846","_rn_":"17"},{"1":"1","2":"1","3":"2","4":"14069","5":"14699","_rn_":"18"},{"1":"1","2":"1","3":"3","4":"14869","5":"15492","_rn_":"19"},{"1":"1","2":"1","3":"2","4":"15712","5":"16377","_rn_":"20"},{"1":"1","2":"1","3":"3","4":"16530","5":"17153","_rn_":"21"},{"1":"1","2":"1","3":"2","4":"17298","5":"17970","_rn_":"22"},{"1":"2","2":"1","3":"5","4":"251","5":"1226","_rn_":"23"},{"1":"2","2":"1","3":"7","4":"1227","5":"1432","_rn_":"24"},{"1":"2","2":"1","3":"4","4":"1433","5":"2221","_rn_":"25"},{"1":"2","2":"1","3":"8","4":"2222","5":"2377","_rn_":"26"},{"1":"2","2":"1","3":"5","4":"2378","5":"3304","_rn_":"27"},{"1":"2","2":"1","3":"11","4":"3305","5":"3572","_rn_":"28"},{"1":"2","2":"1","3":"6","4":"3573","5":"4435","_rn_":"29"},{"1":"2","2":"1","3":"10","4":"4436","5":"4619","_rn_":"30"},{"1":"2","2":"1","3":"4","4":"4620","5":"5452","_rn_":"31"},{"1":"2","2":"1","3":"9","4":"5453","5":"5689","_rn_":"32"},{"1":"2","2":"1","3":"6","4":"5690","5":"6467","_rn_":"33"},{"1":"2","2":"1","3":"12","4":"6468","5":"6709","_rn_":"34"},{"1":"2","2":"1","3":"1","4":"7624","5":"8252","_rn_":"35"},{"1":"2","2":"1","3":"1","4":"8618","5":"9576","_rn_":"36"},{"1":"2","2":"1","3":"1","4":"9991","5":"10927","_rn_":"37"},{"1":"2","2":"1","3":"1","4":"11311","5":"12282","_rn_":"38"},{"1":"2","2":"1","3":"3","4":"13129","5":"13379","_rn_":"39"},{"1":"2","2":"1","3":"3","4":"13495","5":"13927","_rn_":"40"},{"1":"2","2":"1","3":"2","4":"14128","5":"14783","_rn_":"41"},{"1":"2","2":"1","3":"3","4":"15037","5":"15684","_rn_":"42"},{"1":"2","2":"1","3":"2","4":"15920","5":"16598","_rn_":"43"},{"1":"2","2":"1","3":"3","4":"16847","5":"17471","_rn_":"44"},{"1":"2","2":"1","3":"2","4":"17725","5":"18425","_rn_":"45"},{"1":"3","2":"2","3":"5","4":"298","5":"1398","_rn_":"46"},{"1":"3","2":"2","3":"7","4":"1399","5":"1555","_rn_":"47"},{"1":"3","2":"2","3":"4","4":"1686","5":"2627","_rn_":"48"},{"1":"3","2":"2","3":"8","4":"2628","5":"2769","_rn_":"49"},{"1":"3","2":"2","3":"5","4":"2770","5":"3904","_rn_":"50"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
- </script>
- </div>
- ---
- ## TLDR
- #### Потому что очень много препроцессинга и всего такого, мы просто загрузим уже готовый результат
- ```r
- allObservations <- read_rds("allObservations.rds")
- allObservations %>% dim()
- ```
- ```
- ## [1] 1214 5
- ```
- ---
- ## Посмотрим на данные
- ```r
- allObservations %>%
- mutate(recording_length = map_int(data,nrow)) %>%
- ggplot(aes(x = recording_length, y = activityName)) +
- geom_density_ridges(alpha = 0.8)
- ```
- <!-- -->
- ---
- ## Отфильтруем
- ```r
- desiredActivities <- c("STAND_TO_SIT", "SIT_TO_STAND", "SIT_TO_LIE", "LIE_TO_SIT", "STAND_TO_LIE","LIE_TO_STAND")
- filteredObservations <- allObservations %>%
- filter(activityName %in% desiredActivities) %>%
- mutate(observationId = 1:n())
- filteredObservations %>% paged_table()
- ```
- <div data-pagedtable="false">
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- </script>
- </div>
- ---
- ## Разделим на трейн тест
- ```r
- set.seed(100) # seed for reproducibility
- ## get all users
- userIds <- allObservations$userId %>% unique()
- ## randomly choose 24 (80% of 30 individuals) for training
- trainIds <- sample(userIds, size = 24)
- ## set the rest of the users to the testing set
- testIds <- setdiff(userIds,trainIds)
- ## filter data.
- trainData <- filteredObservations %>%
- filter(userId %in% trainIds)
- testData <- filteredObservations %>%
- filter(userId %in% testIds)
- ```
- ---
- layout: true
- ## Посмотрим на графики активности по классам
- ---
- ```r
- unpackedObs <- 1:nrow(trainData) %>%
- map_df(function(rowNum){
- dataRow <- trainData[rowNum, ]
- dataRow$data[[1]] %>%
- mutate(
- activityName = dataRow$activityName,
- observationId = dataRow$observationId,
- time = 1:n() )
- }) %>%
- gather(reading, value, -time, -activityName, -observationId) %>%
- separate(reading, into = c("type", "direction"), sep = "_") %>%
- mutate(type = ifelse(type == "a", "acceleration", "gyro"))
- ```
- ---
- ```r
- unpackedObs %>%
- ggplot(aes(x = time, y = value, color = direction)) +
- geom_line(alpha = 0.2) +
- geom_smooth(se = FALSE, alpha = 0.7, size = 0.5) +
- facet_grid(type ~ activityName, scales = "free_y") +
- theme_minimal() +
- theme( axis.text.x = element_blank() )
- ```
- <img src="Deep_Learning_in_R_files/figure-html/unnamed-chunk-17-1.svg" style="display: block; margin: auto;" />
- ---
- layout: true
- ## Подготовка данных к обучению
- ---
- ```r
- padSize <- trainData$data %>%
- map_int(nrow) %>%
- quantile(p = 0.98) %>%
- ceiling()
- padSize
- ```
- ```
- ## 98%
- ## 334
- ```
- ```r
- convertToTensor <- . %>%
- map(as.matrix) %>%
- pad_sequences(maxlen = padSize)
- trainObs <- trainData$data %>% convertToTensor()
- testObs <- testData$data %>% convertToTensor()
- dim(trainObs)
- ```
- ```
- ## [1] 286 334 6
- ```
- ---
- ```r
- # one hot encoding
- oneHotClasses <- . %>%
- {. - 7} %>% # bring integers down to 0-6 from 7-12
- to_categorical() # One-hot encode
- trainY <- trainData$activity %>% oneHotClasses()
- testY <- testData$activity %>% oneHotClasses()
- ```
- ---
- layout:true
- ## Наконец то сетка!
- ---
- ```r
- input_shape <- dim(trainObs)[-1]
- num_classes <- dim(trainY)[2]
- filters <- 24 # number of convolutional filters to learn
- kernel_size <- 8 # how many time-steps each conv layer sees.
- dense_size <- 48 # size of our penultimate dense layer.
- ```
- ---
- ```r
- model <- keras_model_sequential() # define type of class model
- model %>% layer_conv_1d( # add first convolutions layer
- filters = filters, # num of filters
- kernel_size = kernel_size, # kernel size
- input_shape = input_shape,
- padding = "valid", # to fill padding with zero
- activation = "relu") %>% # activation fiucntion on the end of layer
- layer_batch_normalization() %>% # batch norm
- layer_spatial_dropout_1d(0.15) %>% # dropout 15% neurons
- layer_conv_1d(filters = filters/2, # second convolution layer with half of num filters
- kernel_size = kernel_size,
- activation = "relu") %>%
- layer_global_average_pooling_1d() %>% # to average all verctor representation in one featuremap
- layer_batch_normalization() %>%
- layer_dropout(0.2) %>% # dropout 20% neurons
- layer_dense(dense_size, # fullyconected layer perceptron
- activation = "relu") %>%
- layer_batch_normalization() %>%
- layer_dropout(0.25) %>%
- layer_dense(num_classes, # one more fully connected layer size of num classes
- activation = "softmax", # our loss function for multyply classification
- name = "dense_output")
- ```
- ---
- ### Выведем описание нашей сетки
- ```r
- summary(model)
- ```
- 
- ---
- layout:true
- ## Обучим же наконец
- ---
- ## Компиляция графа
- ```r
- model %>% compile(
- loss = "categorical_crossentropy", # our loss function
- optimizer = "rmsprop", # our optimizer alrorithm
- metrics = "accuracy" # our metric
- )
- ```
- ---
- ## train
- ```r
- trainHistory <- model %>%
- fit(
- x = trainObs, y = trainY, # data
- epochs = 350, # num epoch
- validation_data = list(testObs, testY), # validation tests on each epoch
- callbacks = list(
- callback_model_checkpoint("best_model.h5",
- save_best_only = TRUE))) # update train history and save model
- ```
- ---
- 
- ---
- 
- ---
- layout:true
- ## Предсказание
- ---
- ## Подготовка теста
- ```r
- # one-hot ecnoding labels for predict
- oneHotToLabel <- activityLabels %>%
- mutate(number = number - 7) %>%
- filter(number >= 0) %>%
- mutate(class = paste0("V",number + 1)) %>%
- select(-number)
- ```
- ## Выбор лучшей модели
- ```r
- bestModel <- load_model_hdf5("best_model.h5")
- ```
- ---
- ## Еще немного кода
- ```r
- tidyPredictionProbs <- bestModel %>%
- predict(testObs) %>%
- as_data_frame() %>%
- mutate(obs = 1:n()) %>%
- gather(class, prob, -obs) %>%
- right_join(oneHotToLabel, by = "class")
- predictionPerformance <- tidyPredictionProbs %>%
- group_by(obs) %>%
- summarise(
- highestProb = max(prob),
- predicted = label[prob == highestProb]
- ) %>%
- mutate(
- truth = testData$activityName,
- correct = truth == predicted
- )
- ```
- ---
- ```r
- predictionPerformance %>% paged_table()
- ```
- <div data-pagedtable="false">
- <script data-pagedtable-source type="application/json">
- 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- </script>
- </div>
- ---
- layout:true
- ## Визуализация ошибок
- ---
- ```r
- predictionPerformance %>%
- mutate(result = ifelse(correct, 'Correct', 'Incorrect')) %>%
- ggplot(aes(highestProb)) +
- geom_histogram(binwidth = 0.01) +
- geom_rug(alpha = 0.5) +
- facet_grid(result~.) +
- ggtitle("Probabilities associated with prediction by correctness")
- ```
- <!-- -->
- ---
- ```r
- predictionPerformance %>%
- group_by(truth, predicted) %>%
- summarise(count = n()) %>%
- mutate(good = truth == predicted) %>%
- ggplot(aes(x = truth, y = predicted)) +
- geom_point(aes(size = count, color = good)) +
- geom_text(aes(label = count),
- hjust = 0, vjust = 0,
- nudge_x = 0.1, nudge_y = 0.1) +
- guides(color = FALSE, size = FALSE) +
- theme_minimal()
- ```
- <!-- -->
- ---
- layout:false
- class: inverse, middle, center
- # Заключение
- ---
- background-image: url(https://images.manning.com/720/960/resize/book/a/4e5e97f-4e8d-4d97-a715-f6c2b0eb95f5/Allaire-DLwithR-HI.png)
- ---
- class: center, middle
- # Спасибо!
- Слайды сделаны с помощью R package [**xaringan**](https://github.com/yihui/xaringan).
- Веб версию слайдов можно найти на https://metya.github.io/DeepLearning_in_R/
- Код можно посмотреть здесь https://github.com/metya/DeepLearning_in_R/
- </textarea>
- <script src="https://remarkjs.com/downloads/remark-latest.min.js"></script>
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