Image Recognition & Classification with Keras in R | TensorFlow for Machine Intelligence by Google

Provides steps for applying Image classification & recognition with easy to follow example. GitHub for R code: https://github.com/bkrai/DeepLearningR Data: https://goo.gl/To15db Machine Learning videos: https://goo.gl/WHHqWP To install EBimage package, you can run following 2 lines; install.packages("BiocManager") BiocManager::install("EBImage") Time stamps (provided by Caroline Mimeault): 0:00 Load Packages 0:29 Read Images 4:08 Explore 6:16 Resize 7:23 Reshape 8:45 Row Bind 11:49 One Hot Encoding 12:42 Create Model 16:02 Compile 16:38 Fit Model 18:49 Evaluation and Prediction (train data) 22:20 Evaluation and Prediction (test data) 23:46 True or False questions Uses TensorFlow (by Google) as backend. Includes, - load keras and EBImage packages - read images - explore images and image data - resize and reshape images - one hot encoding - sequential model - compile model - fit model - evaluate model - prediction - confusion matrix Image Classification & Recognition with Keras is an important tool related to analyzing big data or working in data science field. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular. For citation in a research paper, use: Rai BK, (2019). “Advanced Deep Learning with R: Become an expert at designing, building, and improving advanced neural network models using R”, Packt Publishing, ASIN: B07ZFN5MXN.